Violence Exposure, Social Deprivation, and Amygdala-PFC Probtrack Analysis 13 August, 2019
Leigh Gayle Goetschius
Date of Last Edit: 13 August, 2019
R Version Used
R.version.string## [1] "R version 3.5.1 (2018-07-02)"Packages Used
# Table of packages
kable(table[-1,], format = "html", align = "c") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))| Package | Title | Maintainer | Version | URL | |
|---|---|---|---|---|---|
| foreign | Read Data Stored by ‘Minitab’, ‘S’, ‘SAS’, ‘SPSS’, ‘Stata’, ‘Systat’, ‘Weka’, ‘dBase’, … | R Core Team <R-core@R-project.org>; | 0.8-70 | NA | |
| outliers | Tests for outliers | Lukasz Komsta <lukasz.komsta@umlub.pl>; | 0.14 | http://www.r-project.org, | |
| tidyverse | Easily Install and Load the ‘Tidyverse’ | Hadley Wickham <hadley@rstudio.com>; | 1.2.1 | http://tidyverse.tidyverse.org, | |
| knitr | A General-Purpose Package for Dynamic Report Generation in R | Yihui Xie <xie@yihui.name>; | 1.20 | NA | |
| psych | Procedures for Psychological, Psychometric, and Personality Research | William Revelle <revelle@northwestern.edu>; | 1.8.4 | NA | |
| gvlma | Global Validation of Linear Models Assumptions | Elizabeth Slate <slate@stat.fsu.edu>; | 1.0.0.2 | NA | |
| car | Companion to Applied Regression | John Fox <jfox@mcmaster.ca>; | 3.0-0 | https://r-forge.r-project.org/projects/car/, | |
| ggplot2 | Create Elegant Data Visualisations Using the Grammar of Graphics | Hadley Wickham <hadley@rstudio.com>; | 3.0.0 | http://ggplot2.tidyverse.org, | |
| GGally | Extension to ‘ggplot2’ | Barret Schloerke <schloerke@gmail.com>; | 1.4.0 | https://ggobi.github.io/ggally, | |
| data.table |
Extension of data.frame
|
Matt Dowle <mattjdowle@gmail.com>; | 1.11.4 | NA | |
| kableExtra | Construct Complex Table with ‘kable’ and Pipe Syntax | Hao Zhu <haozhu233@gmail.com>; | 0.9.0 | http://haozhu233.github.io/kableExtra/, | |
| jtools | Analysis and Presentation of Social Scientific Data | Jacob A. Long <long.1377@osu.edu>; | 2.0.1 | NA | |
| ggstance | Horizontal ‘ggplot2’ Components | Lionel Henry <lionel@rstudio.com>; | 0.3.1 | NA | |
| huxtable | Easily Create and Style Tables for LaTeX, HTML and Other Formats | David Hugh-Jones <davidhughjones@gmail.com>; | 4.3.0 | NA | |
| NA | NA | NA | NA | NA | |
| interactions | Comprehensive, User-Friendly Toolkit for Probing Interactions | Jacob A. Long <long.1377@osu.edu>; | 1.0.0 | NA |
Load data.
data = read.csv('/Users/leighgayle/Box Sync/ThreatDep_Probtrack_Amygdala/Manuscript/Data&Analysis/ViolenceExposure_SocialDep_AmygdalPFCProbtrack_Data_100218_Final.csv')In the data as it is right now, we’ve got all of the subjects with dMRI data, the violence/dep composites, and the subgroups based on the threat/dep composite scores.
Univariate (rather than multivariate) Outlier Method
Right hemisphere outliers
fit_R47 = lm(RAmy_BA47 ~ VE * Deprivation, data = data)
cutoff = 4/((nrow(data) - length(fit_R47$coefficients)-1))
plot(fit_R47, cook.levels=cutoff)
fit_R10 = lm(RAmy_BA10 ~ VE * Deprivation, data = data)
cutoff = 4/((nrow(data) - length(fit_R10$coefficients)-1))
plot(fit_R10, cook.levels=cutoff)
clean.data = data[-c(104, 108, 115, 116, 130, 151), ]Left hemisphere outliers
fit_L11 = lm(LAmy_BA11 ~ VE * Deprivation, data = data)
cutoff = 4/((nrow(data) - length(fit_L11$coefficients)-1))
plot(fit_L11, cook.levels=cutoff)
fit_L10 = lm(LAmy_BA10 ~ VE * Deprivation, data = data)
cutoff = 4/((nrow(data) - length(fit_L10$coefficients)-1))
plot(fit_L10, cook.levels=cutoff)
clean.dataL = data[-c(22, 38, 73, 116, 131, 144), ]Right hemisphere models
Testing amygdala-BA47 model.
# All of my covariates
summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))##
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean + ALES_sum +
## cm1edu + m1b2, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12858 -0.05172 -0.01624 0.05144 0.20952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1116331 0.0567353 1.968 0.050779 .
## VE 0.0083691 0.0128228 0.653 0.514870
## Deprivation 0.0191896 0.0137379 1.397 0.164328
## EthnoRace_C 0.0146365 0.0225407 0.649 0.517019
## EthnoRace_AA -0.0188106 0.0178671 -1.053 0.293959
## Gender_0F_1M 0.0060139 0.0145778 0.413 0.680479
## RAmy_BA10 0.5200951 0.1395541 3.727 0.000266 ***
## Internalizing 0.0135273 0.0148590 0.910 0.363944
## pubc_mean 0.0039379 0.0120704 0.326 0.744650
## ALES_sum -0.0011073 0.0011051 -1.002 0.317836
## cm1edu -0.0004614 0.0060109 -0.077 0.938911
## m1b2 -0.0140689 0.0149198 -0.943 0.347067
## VE:Deprivation -0.0373562 0.0151257 -2.470 0.014534 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07242 on 166 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.1411, Adjusted R-squared: 0.07897
## F-statistic: 2.272 on 12 and 166 DF, p-value: 0.01078# Preregistered covariates
summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + cm1edu + m1b2, data=clean.data))##
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + RAmy_BA10 + cm1edu + m1b2, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12911 -0.05300 -0.01530 0.04955 0.21856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1093016 0.0350789 3.116 0.002155 **
## VE 0.0059499 0.0125674 0.473 0.636514
## Deprivation 0.0220136 0.0133989 1.643 0.102255
## EthnoRace_C 0.0170367 0.0223367 0.763 0.446691
## EthnoRace_AA -0.0175410 0.0177375 -0.989 0.324115
## Gender_0F_1M 0.0028815 0.0111333 0.259 0.796093
## RAmy_BA10 0.5301232 0.1386121 3.825 0.000184 ***
## cm1edu 0.0004848 0.0059309 0.082 0.934945
## m1b2 -0.0130476 0.0148080 -0.881 0.379507
## VE:Deprivation -0.0375484 0.0150525 -2.494 0.013574 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07209 on 169 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.1334, Adjusted R-squared: 0.0873
## F-statistic: 2.892 on 9 and 169 DF, p-value: 0.003323# Not adjusting for RAmy_BA10
# All of my covariates
summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))##
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu +
## m1b2, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11937 -0.05567 -0.02096 0.05154 0.22100
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.143878 0.058195 2.472 0.01443 *
## VE 0.009247 0.013306 0.695 0.48805
## Deprivation 0.019851 0.014257 1.392 0.16567
## EthnoRace_C 0.014463 0.023394 0.618 0.53726
## EthnoRace_AA -0.008562 0.018323 -0.467 0.64091
## Gender_0F_1M 0.005922 0.015130 0.391 0.69597
## Internalizing 0.016199 0.015404 1.052 0.29448
## pubc_mean 0.003325 0.012526 0.265 0.79102
## ALES_sum -0.001339 0.001145 -1.169 0.24402
## cm1edu -0.003299 0.006188 -0.533 0.59466
## m1b2 -0.016231 0.015473 -1.049 0.29570
## VE:Deprivation -0.044704 0.015565 -2.872 0.00461 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07516 on 167 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.0692, Adjusted R-squared: 0.007885
## F-statistic: 1.129 on 11 and 167 DF, p-value: 0.342# No covariates included
summary(lm(RAmy_BA47 ~ VE * Deprivation, data=clean.data))##
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10242 -0.05713 -0.01939 0.04961 0.22930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.103768 0.005771 17.980 < 2e-16 ***
## VE 0.001060 0.012183 0.087 0.93076
## Deprivation 0.020694 0.013462 1.537 0.12600
## VE:Deprivation -0.038615 0.014649 -2.636 0.00913 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07431 on 179 degrees of freedom
## Multiple R-squared: 0.03983, Adjusted R-squared: 0.02374
## F-statistic: 2.475 on 3 and 179 DF, p-value: 0.06306# Correlations between amygdala-BA47 probtrack and environmental composites.
cor.test(clean.data$RAmy_BA47, clean.data$VE)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA47 and clean.data$VE
## t = -0.19033, df = 181, p-value = 0.8493
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1588767 0.1311798
## sample estimates:
## cor
## -0.01414606cor.test(clean.data$RAmy_BA47, clean.data$Deprivation)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA47 and clean.data$Deprivation
## t = 0.47278, df = 181, p-value = 0.6369
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.110500 0.179263
## sample estimates:
## cor
## 0.03511956# Correlation between amyygdala-BA47 probtrack and amygdala activation to threat faces.
cor.test(clean.data$Ramy_0035, clean.data$RAmy_BA47)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_0035 and clean.data$RAmy_BA47
## t = -3.712, df = 150, p-value = 0.0002892
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4294252 -0.1371882
## sample estimates:
## cor
## -0.290054# Including covariates.
summary(lm(Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, data = clean.data))##
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean,
## data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.57781 -0.39097 0.03322 0.34232 1.93497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.20961 0.30398 3.979 0.000108 ***
## RAmy_BA47 -2.64476 0.69467 -3.807 0.000205 ***
## Internalizing 0.18762 0.12282 1.528 0.128743
## pubc_mean -0.03291 0.09005 -0.365 0.715299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6428 on 148 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.09877, Adjusted R-squared: 0.0805
## F-statistic: 5.407 on 3 and 148 DF, p-value: 0.001475actmod1 = lm(Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, data = clean.data)
lm.beta(actmod1)## RAmy_BA47 Internalizing pubc_mean
## -0.29776239 0.11973725 -0.02858212summary(lm(Ramy_0035 ~ RAmy_BA47 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.data))##
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA47 + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu +
## m1b2, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.53444 -0.38330 0.04329 0.35252 2.03119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.61727 0.54665 1.129 0.260761
## RAmy_BA47 -2.59122 0.70519 -3.674 0.000339 ***
## EthnoRace_C 0.02034 0.20578 0.099 0.921391
## EthnoRace_AA 0.04680 0.16286 0.287 0.774288
## Gender_0F_1M 0.18210 0.15019 1.212 0.227388
## Internalizing 0.18179 0.14546 1.250 0.213471
## pubc_mean 0.06690 0.11977 0.559 0.577370
## ALES_sum 0.01712 0.01060 1.616 0.108392
## cm1edu -0.08734 0.05658 -1.544 0.124921
## m1b2 0.08718 0.14604 0.597 0.551521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6427 on 139 degrees of freedom
## (34 observations deleted due to missingness)
## Multiple R-squared: 0.1514, Adjusted R-squared: 0.09649
## F-statistic: 2.756 on 9 and 139 DF, p-value: 0.005382RBA47_act_mod = lm(Ramy_0035 ~ RAmy_BA47 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.data)
lm.beta(RBA47_act_mod)## RAmy_BA47 EthnoRace_C EthnoRace_AA Gender_0F_1M Internalizing
## -0.29113279 0.01071021 0.03124486 0.13460849 0.11346563
## pubc_mean ALES_sum cm1edu m1b2
## 0.05786410 0.13877999 -0.13017061 0.05054025# Plot association between amygdala-BA47 connectivity and amygdala activation to threat faces.
plot(clean.data$RAmy_BA47, clean.data$Ramy_0035, xlab = 'Amygdala-OFC White Matter Connectivity', ylab = 'Amygdala Activation', frame.plot = FALSE)
abline(lm(clean.data$Ramy_0035 ~ clean.data$RAmy_BA47))
# Check the correlation between amygdala-BA47 probtrack and amygdala activation to angry faces and to fearful faces.
cor.test(clean.data$RAmy_0011, clean.data$RAmy_BA47)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_0011 and clean.data$RAmy_BA47
## t = -2.6634, df = 150, p-value = 0.00858
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.35953783 -0.05516595
## sample estimates:
## cor
## -0.2125004summary(lm(RAmy_0011 ~ RAmy_BA47, data = clean.data))##
## Call:
## lm(formula = RAmy_0011 ~ RAmy_BA47, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18775 -0.23439 -0.01549 0.25184 0.92374
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.54245 0.05447 9.959 < 2e-16 ***
## RAmy_BA47 -1.14056 0.42823 -2.663 0.00858 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3971 on 150 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.04516, Adjusted R-squared: 0.03879
## F-statistic: 7.094 on 1 and 150 DF, p-value: 0.00858cor.test(clean.data$Ramy_0003, clean.data$RAmy_BA47)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_0003 and clean.data$RAmy_BA47
## t = -1.9561, df = 150, p-value = 0.05231
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.309155503 0.001521612
## sample estimates:
## cor
## -0.1577173summary(lm(Ramy_0003 ~ RAmy_BA47, data = clean.data))##
## Call:
## lm(formula = Ramy_0003 ~ RAmy_BA47, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.32673 -0.22264 -0.00822 0.19260 1.33566
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.45027 0.05546 8.119 1.59e-13 ***
## RAmy_BA47 -0.85291 0.43602 -1.956 0.0523 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4044 on 150 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.02487, Adjusted R-squared: 0.01837
## F-statistic: 3.826 on 1 and 150 DF, p-value: 0.05231summary(lm(Ramy_0003 ~ RAmy_BA47 + Internalizing + pubc_mean, data = clean.data))##
## Call:
## lm(formula = Ramy_0003 ~ RAmy_BA47 + Internalizing + pubc_mean,
## data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.35948 -0.23180 -0.02618 0.20078 1.27111
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.72460 0.19077 3.798 0.000212 ***
## RAmy_BA47 -0.86961 0.43596 -1.995 0.047913 *
## Internalizing 0.06074 0.07708 0.788 0.431928
## pubc_mean -0.08454 0.05652 -1.496 0.136831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4034 on 148 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.04243, Adjusted R-squared: 0.02302
## F-statistic: 2.186 on 3 and 148 DF, p-value: 0.09211Show that the residuals of this model look random for Amygdala-BA47 model.
mod = summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean + ALES_sum, data=clean.data))
fitBA47 = lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data)
fit_int = lm(RAmy_BA47 ~ VE * Deprivation, data = clean.data)
fit_prereg = lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + cm1edu + m1b2, data=clean.data)
# To get the beta weights
lm.beta(fitBA47)## Warning in b * sx: longer object length is not a multiple of shorter object
## length## VE Deprivation EthnoRace_C EthnoRace_AA Gender_0F_1M
## 0.059253261 0.128960058 0.067425997 -0.110001820 0.039820543
## RAmy_BA10 Internalizing pubc_mean ALES_sum cm1edu
## 0.281265469 0.074013282 0.030468099 -0.078266731 -0.006296673
## m1b2 VE:Deprivation
## -0.076457958 -0.264482541lm.beta(fit_int)## Warning in b * sx: longer object length is not a multiple of shorter object
## length## VE Deprivation VE:Deprivation
## 0.007518361 0.138282871 -0.273872284lm.beta(fit_prereg)## Warning in b * sx: longer object length is not a multiple of shorter object
## length## VE Deprivation EthnoRace_C EthnoRace_AA Gender_0F_1M
## 0.04212513 0.14793806 0.07848330 -0.10257709 0.01907941
## RAmy_BA10 cm1edu m1b2 VE:Deprivation
## 0.28668864 0.00661693 -0.07090731 -0.26584321plot(mod$residuals)
mean(mod$residuals) # the mean of the residuals are zero## [1] -3.277231e-18t.test(mod$residuals)##
## One Sample t-test
##
## data: mod$residuals
## t = -6.3873e-16, df = 182, p-value = 1
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.0101236 0.0101236
## sample estimates:
## mean of x
## -3.277231e-18dwtest(fitBA47)##
## Durbin-Watson test
##
## data: fitBA47
## DW = 1.918, p-value = 0.2667
## alternative hypothesis: true autocorrelation is greater than 0cor.test(mod$residuals, clean.data$Deprivation*clean.data$VE)##
## Pearson's product-moment correlation
##
## data: mod$residuals and clean.data$Deprivation * clean.data$VE
## t = 2.1481e-16, df = 181, p-value = 1
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1450566 0.1450566
## sample estimates:
## cor
## 1.596658e-17par(family = 'serif')
qqnorm(mod$residuals, frame.plot = FALSE, main=NULL, cex.lab = 1.5, font.lab=2)
Testing amygdala-BA10 model.
# All of my covariates
summary(lm(RAmy_BA10 ~ VE * Deprivation + RAmy_BA47 + EthnoRace_AA + EthnoRace_C + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))##
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation + RAmy_BA47 + EthnoRace_AA +
## EthnoRace_C + Gender_0F_1M + Internalizing + pubc_mean +
## ALES_sum + cm1edu + m1b2, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053192 -0.029054 -0.005352 0.016847 0.124212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0406384 0.0305004 1.332 0.184558
## VE 0.0003154 0.0068595 0.046 0.963379
## Deprivation -0.0016757 0.0073815 -0.227 0.820698
## RAmy_BA47 0.1484542 0.0398338 3.727 0.000266 ***
## EthnoRace_AA 0.0209766 0.0094382 2.223 0.027598 *
## EthnoRace_C -0.0024801 0.0120564 -0.206 0.837274
## Gender_0F_1M -0.0010549 0.0077919 -0.135 0.892470
## Internalizing 0.0027326 0.0079556 0.343 0.731668
## pubc_mean -0.0016726 0.0064495 -0.259 0.795694
## ALES_sum -0.0002465 0.0005919 -0.417 0.677570
## cm1edu -0.0049664 0.0031882 -1.558 0.121203
## m1b2 -0.0017475 0.0079913 -0.219 0.827169
## VE:Deprivation -0.0074915 0.0082077 -0.913 0.362698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03869 on 166 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.1617, Adjusted R-squared: 0.1011
## F-statistic: 2.668 on 12 and 166 DF, p-value: 0.00266# Not adjusting for RAmy_BA47
# All of my covariates
summary(lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))##
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C +
## Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu +
## m1b2, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.058827 -0.031378 -0.009747 0.016993 0.123887
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0619976 0.0310916 1.994 0.0478 *
## VE 0.0016882 0.0071090 0.237 0.8126
## Deprivation 0.0012713 0.0076170 0.167 0.8677
## EthnoRace_AA 0.0197056 0.0097892 2.013 0.0457 *
## EthnoRace_C -0.0003329 0.0124987 -0.027 0.9788
## Gender_0F_1M -0.0001757 0.0080833 -0.022 0.9827
## Internalizing 0.0051375 0.0082297 0.624 0.5333
## pubc_mean -0.0011791 0.0066924 -0.176 0.8604
## ALES_sum -0.0004453 0.0006118 -0.728 0.4677
## cm1edu -0.0054562 0.0033062 -1.650 0.1008
## m1b2 -0.0041571 0.0082667 -0.503 0.6157
## VE:Deprivation -0.0141280 0.0083156 -1.699 0.0912 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04015 on 167 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.09155, Adjusted R-squared: 0.03171
## F-statistic: 1.53 on 11 and 167 DF, p-value: 0.1249# No covariates
summary(lm(RAmy_BA10 ~ VE * Deprivation, data = clean.data))##
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052020 -0.033268 -0.009264 0.023304 0.124582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.050212 0.003184 15.772 <2e-16 ***
## VE 0.005649 0.006721 0.841 0.402
## Deprivation 0.006325 0.007426 0.852 0.396
## VE:Deprivation -0.020776 0.008081 -2.571 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.041 on 179 degrees of freedom
## Multiple R-squared: 0.03612, Adjusted R-squared: 0.01996
## F-statistic: 2.236 on 3 and 179 DF, p-value: 0.0857# Correlation between amyygdala-BA10 probtrack and amygdala activation to threat faces.
cor.test(clean.data$Ramy_0035, clean.data$RAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_0035 and clean.data$RAmy_BA10
## t = -2.5913, df = 150, p-value = 0.01051
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.35451431 -0.04942427
## sample estimates:
## cor
## -0.2069962cor.test(clean.data$Ramy_0003, clean.data$RAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_0003 and clean.data$RAmy_BA10
## t = -2.0829, df = 150, p-value = 0.03896
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.318363596 -0.008691851
## sample estimates:
## cor
## -0.1676606cor.test(clean.data$RAmy_0011, clean.data$RAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_0011 and clean.data$RAmy_BA10
## t = -2.3473, df = 150, p-value = 0.02022
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33732032 -0.02992375
## sample estimates:
## cor
## -0.1882278summary(lm(RAmy_0011 ~ RAmy_BA10, data = clean.data))##
## Call:
## lm(formula = RAmy_0011 ~ RAmy_BA10, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.10669 -0.22345 -0.01279 0.23598 0.94944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.51278 0.04931 10.398 <2e-16 ***
## RAmy_BA10 -1.86684 0.79532 -2.347 0.0202 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3991 on 150 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.03543, Adjusted R-squared: 0.029
## F-statistic: 5.51 on 1 and 150 DF, p-value: 0.02022summary(lm(Ramy_0003 ~ RAmy_BA10, data = clean.data))##
## Call:
## lm(formula = Ramy_0003 ~ RAmy_BA10, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.29170 -0.22899 -0.02281 0.22370 1.34640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.44114 0.04987 8.845 2.33e-15 ***
## RAmy_BA10 -1.67540 0.80436 -2.083 0.039 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4037 on 150 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.02811, Adjusted R-squared: 0.02163
## F-statistic: 4.338 on 1 and 150 DF, p-value: 0.03896# Including covariates.
summary(lm(Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, data = clean.data))##
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean,
## data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8839 -0.3438 -0.0134 0.3413 1.9457
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.12478 0.31059 3.621 0.000402 ***
## RAmy_BA10 -3.46554 1.31250 -2.640 0.009169 **
## Internalizing 0.16775 0.12556 1.336 0.183597
## pubc_mean -0.04034 0.09224 -0.437 0.662457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6582 on 148 degrees of freedom
## (31 observations deleted due to missingness)
## Multiple R-squared: 0.05502, Adjusted R-squared: 0.03586
## F-statistic: 2.872 on 3 and 148 DF, p-value: 0.03838actmod2 = lm(Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, data = clean.data)
lm.beta(actmod2)## RAmy_BA10 Internalizing pubc_mean
## -0.21115053 0.10705551 -0.03503934summary(lm(Ramy_0035 ~ RAmy_BA10 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.data))##
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA10 + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu +
## m1b2, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.80557 -0.36918 0.01096 0.33275 2.08432
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.465308 0.549225 0.847 0.39834
## RAmy_BA10 -4.214961 1.364128 -3.090 0.00242 **
## EthnoRace_C -0.002073 0.208587 -0.010 0.99209
## EthnoRace_AA 0.103482 0.167135 0.619 0.53683
## Gender_0F_1M 0.161582 0.151980 1.063 0.28954
## Internalizing 0.138989 0.146566 0.948 0.34462
## pubc_mean 0.046589 0.121293 0.384 0.70149
## ALES_sum 0.020123 0.010681 1.884 0.06165 .
## cm1edu -0.094702 0.057615 -1.644 0.10250
## m1b2 0.145896 0.147381 0.990 0.32393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6512 on 139 degrees of freedom
## (34 observations deleted due to missingness)
## Multiple R-squared: 0.1288, Adjusted R-squared: 0.07243
## F-statistic: 2.284 on 9 and 139 DF, p-value: 0.02022RBA10_act_mod = lm(Ramy_0035 ~ RAmy_BA10 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.data)
lm.beta(RBA10_act_mod)## RAmy_BA10 EthnoRace_C EthnoRace_AA Gender_0F_1M Internalizing
## -0.255566513 -0.001091307 0.069093890 0.119438883 0.086750104
## pubc_mean ALES_sum cm1edu m1b2
## 0.040297079 0.163073476 -0.141144444 0.084580538Looking at the residuals of the amygdala-BA10 model.
mod1 = summary(lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_AA+ EthnoRace_C + Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data))
mod10 = lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_AA+ EthnoRace_C + Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data=clean.data)
mod_int = lm(RAmy_BA10 ~ VE * Deprivation, data = clean.data)
summary(mod_int)##
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.052020 -0.033268 -0.009264 0.023304 0.124582
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.050212 0.003184 15.772 <2e-16 ***
## VE 0.005649 0.006721 0.841 0.402
## Deprivation 0.006325 0.007426 0.852 0.396
## VE:Deprivation -0.020776 0.008081 -2.571 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.041 on 179 degrees of freedom
## Multiple R-squared: 0.03612, Adjusted R-squared: 0.01996
## F-statistic: 2.236 on 3 and 179 DF, p-value: 0.0857plot(mod1$residuals)
Just a fun plot of what the RAmy_BA10 interaction looks like before covariates – it seems to look really similar to the RAmy-BA47 interaction. That’s a fun sanity check.
interact_plot(mod_int, pred = VE, modx = Deprivation, data = clean.data, modx.values = 'plus-minus')
Left hemisphere models
Amygdala-BA10 model
# Including all covariates
summary(lm(LAmy_BA10 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + Gender_0F_1M + LAmy_BA11 + Internalizing + pubc_mean + CTQ_Abuse + CTQ.Neglect, data=clean.dataL))##
## Call:
## lm(formula = LAmy_BA10 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C +
## Gender_0F_1M + LAmy_BA11 + Internalizing + pubc_mean + CTQ_Abuse +
## CTQ.Neglect, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.026873 -0.005284 -0.002192 0.000822 0.057775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.384e-03 8.976e-03 0.934 0.352
## VE 1.127e-03 2.266e-03 0.497 0.620
## Deprivation -5.298e-04 2.575e-03 -0.206 0.837
## EthnoRace_AA 3.081e-03 3.083e-03 0.999 0.319
## EthnoRace_C 3.145e-03 3.870e-03 0.813 0.418
## Gender_0F_1M -3.440e-03 2.513e-03 -1.369 0.173
## LAmy_BA11 3.840e-01 3.975e-02 9.661 <2e-16 ***
## Internalizing 2.726e-03 2.596e-03 1.050 0.295
## pubc_mean -2.198e-03 2.136e-03 -1.029 0.305
## CTQ_Abuse -5.525e-05 1.901e-04 -0.291 0.772
## CTQ.Neglect 1.129e-04 1.701e-04 0.664 0.508
## VE:Deprivation -2.561e-03 2.649e-03 -0.967 0.335
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01261 on 165 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.3975, Adjusted R-squared: 0.3573
## F-statistic: 9.896 on 11 and 165 DF, p-value: 1.121e-13# No covariates
summary(lm(LAmy_BA10 ~ VE * Deprivation, data = clean.dataL))##
## Call:
## lm(formula = LAmy_BA10 ~ VE * Deprivation, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.011388 -0.008976 -0.006239 0.002917 0.069315
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.085e-02 1.212e-03 8.958 4.24e-16 ***
## VE 3.945e-05 2.575e-03 0.015 0.988
## Deprivation 1.042e-03 3.036e-03 0.343 0.732
## VE:Deprivation -4.285e-03 3.031e-03 -1.414 0.159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01557 on 179 degrees of freedom
## Multiple R-squared: 0.01196, Adjusted R-squared: -0.004596
## F-statistic: 0.7225 on 3 and 179 DF, p-value: 0.5398# Association between amygdala-BA10 and amygdala activation to threat faces.
cor.test(clean.dataL$Lamy_0035, clean.dataL$LAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.dataL$Lamy_0035 and clean.dataL$LAmy_BA10
## t = -3.9734, df = 151, p-value = 0.0001094
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4446393 -0.1566375
## sample estimates:
## cor
## -0.3076685# Including covariates.
summary(lm(Lamy_0035 ~ LAmy_BA10 + Internalizing + pubc_mean, data = clean.dataL))##
## Call:
## lm(formula = Lamy_0035 ~ LAmy_BA10 + Internalizing + pubc_mean,
## data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.54665 -0.37617 -0.06264 0.31400 1.37916
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.03211 0.25861 3.991 0.000103 ***
## LAmy_BA10 -12.65472 3.03595 -4.168 5.18e-05 ***
## Internalizing 0.19112 0.10908 1.752 0.081811 .
## pubc_mean -0.03754 0.07794 -0.482 0.630746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5634 on 149 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.1138, Adjusted R-squared: 0.09597
## F-statistic: 6.379 on 3 and 149 DF, p-value: 0.0004269actmod3 = lm(Lamy_0035 ~ LAmy_BA10 + Internalizing + pubc_mean, data = clean.dataL)
lm.beta(actmod3)## LAmy_BA10 Internalizing pubc_mean
## -0.3243390 0.1364928 -0.0372194summary(lm(Lamy_0035 ~ LAmy_BA10 + EthnoRace_AA + EthnoRace_C + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.dataL))##
## Call:
## lm(formula = Lamy_0035 ~ LAmy_BA10 + EthnoRace_AA + EthnoRace_C +
## Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu +
## m1b2, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.42218 -0.38257 -0.01476 0.28558 1.43171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.704643 0.475121 1.483 0.140
## LAmy_BA10 -12.748139 3.160210 -4.034 8.98e-05 ***
## EthnoRace_AA 0.174127 0.140207 1.242 0.216
## EthnoRace_C 0.117996 0.178041 0.663 0.509
## Gender_0F_1M 0.083592 0.130791 0.639 0.524
## Internalizing 0.176301 0.129938 1.357 0.177
## pubc_mean 0.005813 0.104104 0.056 0.956
## ALES_sum 0.012061 0.009424 1.280 0.203
## cm1edu -0.073627 0.049221 -1.496 0.137
## m1b2 0.024669 0.127816 0.193 0.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.566 on 140 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1568, Adjusted R-squared: 0.1026
## F-statistic: 2.893 on 9 and 140 DF, p-value: 0.003622LBA10_act_mod = lm(Lamy_0035 ~ LAmy_BA10 + EthnoRace_AA + EthnoRace_C + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.dataL)
lm.beta(LBA10_act_mod)## LAmy_BA10 EthnoRace_AA EthnoRace_C Gender_0F_1M Internalizing
## -0.326768500 0.132235389 0.070103193 0.069966237 0.123104466
## pubc_mean ALES_sum cm1edu m1b2
## 0.005741941 0.110948372 -0.124587967 0.016142258summary(lm(LAmy_0011 ~ LAmy_BA10, data = clean.dataL))##
## Call:
## lm(formula = LAmy_0011 ~ LAmy_BA10, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.05174 -0.18910 -0.04617 0.22286 1.09043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.45078 0.03549 12.702 <2e-16 ***
## LAmy_BA10 -5.53465 1.93360 -2.862 0.0048 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3621 on 151 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.05147, Adjusted R-squared: 0.04518
## F-statistic: 8.193 on 1 and 151 DF, p-value: 0.004803summary(lm(Lamy_0003 ~ LAmy_BA10, data = clean.dataL))##
## Call:
## lm(formula = Lamy_0003 ~ LAmy_BA10, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.43252 -0.23424 -0.03496 0.24835 1.22662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.47800 0.03974 12.03 < 2e-16 ***
## LAmy_BA10 -6.47473 2.16536 -2.99 0.00326 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4055 on 151 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.0559, Adjusted R-squared: 0.04965
## F-statistic: 8.941 on 1 and 151 DF, p-value: 0.003257Checking the residuals of the amygdala-BA10 model
mod2 = summary(lm(LAmy_BA10 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + LAmy_BA11 + Internalizing + pubc_mean, data=clean.dataL))
plot(mod2$residuals)
Amygdala-BA11 Model
# Including all covariates.
summary(lm(LAmy_BA11 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C + Gender_1F_2M + LAmy_BA10 + Internalizing + pubc_mean + CTQ_Abuse + CTQ.Neglect, data=clean.dataL))##
## Call:
## lm(formula = LAmy_BA11 ~ VE * Deprivation + EthnoRace_AA + EthnoRace_C +
## Gender_1F_2M + LAmy_BA10 + Internalizing + pubc_mean + CTQ_Abuse +
## CTQ.Neglect, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.048043 -0.009621 -0.004978 0.003858 0.082360
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0010769 0.0167095 -0.064 0.949
## VE -0.0019776 0.0035458 -0.558 0.578
## Deprivation 0.0006865 0.0040309 0.170 0.865
## EthnoRace_AA 0.0019664 0.0048372 0.407 0.685
## EthnoRace_C 0.0002974 0.0060692 0.049 0.961
## Gender_1F_2M 0.0052831 0.0039343 1.343 0.181
## LAmy_BA10 0.9408491 0.0973833 9.661 <2e-16 ***
## Internalizing 0.0025730 0.0040721 0.632 0.528
## pubc_mean 0.0033019 0.0033442 0.987 0.325
## CTQ_Abuse -0.0003311 0.0002964 -1.117 0.266
## CTQ.Neglect -0.0001699 0.0002663 -0.638 0.524
## VE:Deprivation -0.0008885 0.0041569 -0.214 0.831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01973 on 165 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.3916, Adjusted R-squared: 0.351
## F-statistic: 9.653 on 11 and 165 DF, p-value: 2.353e-13# No covariates.
summary(lm(LAmy_BA11 ~ VE * Deprivation, data = clean.dataL))##
## Call:
## lm(formula = LAmy_BA11 ~ VE * Deprivation, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.020188 -0.015841 -0.009986 0.006083 0.092431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.019927 0.001898 10.502 <2e-16 ***
## VE -0.001851 0.004032 -0.459 0.647
## Deprivation 0.001581 0.004754 0.333 0.740
## VE:Deprivation -0.005120 0.004746 -1.079 0.282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02438 on 179 degrees of freedom
## Multiple R-squared: 0.01017, Adjusted R-squared: -0.006421
## F-statistic: 0.6129 on 3 and 179 DF, p-value: 0.6075# Association between amygdala-BA11 connectivity and amygdala activation to threat faces.
cor.test(clean.dataL$Lamy_0035, clean.dataL$LAmy_BA11)##
## Pearson's product-moment correlation
##
## data: clean.dataL$Lamy_0035 and clean.dataL$LAmy_BA11
## t = -3.4424, df = 151, p-value = 0.0007461
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4108459 -0.1160426
## sample estimates:
## cor
## -0.2697536# Including covariates.
summary(lm(Lamy_0035 ~ LAmy_BA11 + Internalizing + pubc_mean, data = clean.dataL))##
## Call:
## lm(formula = Lamy_0035 ~ LAmy_BA11 + Internalizing + pubc_mean,
## data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.61737 -0.38847 -0.04459 0.30089 1.51358
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.99552 0.26242 3.794 0.000215 ***
## LAmy_BA11 -6.31347 1.82545 -3.459 0.000708 ***
## Internalizing 0.14580 0.11004 1.325 0.187215
## pubc_mean -0.02731 0.07939 -0.344 0.731349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5728 on 149 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.08401, Adjusted R-squared: 0.06557
## F-statistic: 4.555 on 3 and 149 DF, p-value: 0.004387actmod4 = lm(Lamy_0035 ~ LAmy_BA11 + Internalizing + pubc_mean, data = clean.dataL)
lm.beta(actmod4)## LAmy_BA11 Internalizing pubc_mean
## -0.27197503 0.10412214 -0.02707385summary(lm(Lamy_0035 ~ LAmy_BA11 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.dataL))##
## Call:
## lm(formula = Lamy_0035 ~ LAmy_BA11 + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu +
## m1b2, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.56844 -0.35610 -0.02711 0.27057 1.44881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.542496 0.475775 1.140 0.256135
## LAmy_BA11 -6.823881 1.867726 -3.654 0.000365 ***
## EthnoRace_C 0.109707 0.179681 0.611 0.542478
## EthnoRace_AA 0.155514 0.141137 1.102 0.272410
## Gender_0F_1M 0.164690 0.132415 1.244 0.215672
## Internalizing 0.129310 0.129718 0.997 0.320555
## pubc_mean 0.062969 0.105764 0.595 0.552559
## ALES_sum 0.016969 0.009355 1.814 0.071815 .
## cm1edu -0.080555 0.049883 -1.615 0.108591
## m1b2 -0.018202 0.130031 -0.140 0.888878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5713 on 140 degrees of freedom
## (33 observations deleted due to missingness)
## Multiple R-squared: 0.1407, Adjusted R-squared: 0.08551
## F-statistic: 2.548 on 9 and 140 DF, p-value: 0.009679LBA11_act_mod = lm(Lamy_0035 ~ LAmy_BA11 + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + Internalizing + pubc_mean + ALES_sum + cm1edu + m1b2, data = clean.dataL)
lm.beta(LBA11_act_mod)## LAmy_BA11 EthnoRace_C EthnoRace_AA Gender_0F_1M Internalizing
## -0.29333960 0.06517835 0.11810029 0.13784408 0.09029230
## pubc_mean ALES_sum cm1edu m1b2
## 0.06219860 0.15609964 -0.13631212 -0.01191030summary(lm(LAmy_0011 ~ LAmy_BA11, data = clean.dataL))##
## Call:
## lm(formula = LAmy_0011 ~ LAmy_BA11, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.00472 -0.20670 -0.04196 0.23181 1.10276
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43874 0.03791 11.574 <2e-16 ***
## LAmy_BA11 -2.24835 1.16695 -1.927 0.0559 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3673 on 151 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.02399, Adjusted R-squared: 0.01753
## F-statistic: 3.712 on 1 and 151 DF, p-value: 0.0559summary(lm(Lamy_0003 ~ LAmy_BA11, data = clean.dataL))##
## Call:
## lm(formula = Lamy_0003 ~ LAmy_BA11, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.33227 -0.26554 -0.04716 0.27339 1.21842
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.49688 0.04157 11.95 < 2e-16 ***
## LAmy_BA11 -4.26201 1.27972 -3.33 0.00109 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4028 on 151 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.06843, Adjusted R-squared: 0.06226
## F-statistic: 11.09 on 1 and 151 DF, p-value: 0.00109Checking the residuals for the amygdala-BA11 model.
mod3 = summary(lm(LAmy_BA11 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + LAmy_BA10 + Internalizing, data=clean.dataL))
plot(mod3$residuals)
Parsing the interaction for RAmy_BA47.
Calculating basic statistics to parse the interaction.
model = lm(RAmy_BA47 ~ VE * Deprivation, data = clean.data)
model2 = lm(clean.data$RAmy_BA47 ~ clean.data$Deprivation * clean.data$VE)
vcov(model)## (Intercept) VE Deprivation VE:Deprivation
## (Intercept) 3.330648e-05 1.236356e-06 6.341440e-06 -2.517358e-05
## VE 1.236356e-06 1.484268e-04 -6.211801e-05 -3.870033e-05
## Deprivation 6.341440e-06 -6.211801e-05 1.812183e-04 -6.851916e-05
## VE:Deprivation -2.517358e-05 -3.870033e-05 -6.851916e-05 2.145924e-04summary(model)##
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10242 -0.05713 -0.01939 0.04961 0.22930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.103768 0.005771 17.980 < 2e-16 ***
## VE 0.001060 0.012183 0.087 0.93076
## Deprivation 0.020694 0.013462 1.537 0.12600
## VE:Deprivation -0.038615 0.014649 -2.636 0.00913 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07431 on 179 degrees of freedom
## Multiple R-squared: 0.03983, Adjusted R-squared: 0.02374
## F-statistic: 2.475 on 3 and 179 DF, p-value: 0.06306vcov(model2)## (Intercept) clean.data$Deprivation
## (Intercept) 3.330648e-05 6.341440e-06
## clean.data$Deprivation 6.341440e-06 1.812183e-04
## clean.data$VE 1.236356e-06 -6.211801e-05
## clean.data$Deprivation:clean.data$VE -2.517358e-05 -6.851916e-05
## clean.data$VE
## (Intercept) 1.236356e-06
## clean.data$Deprivation -6.211801e-05
## clean.data$VE 1.484268e-04
## clean.data$Deprivation:clean.data$VE -3.870033e-05
## clean.data$Deprivation:clean.data$VE
## (Intercept) -2.517358e-05
## clean.data$Deprivation -6.851916e-05
## clean.data$VE -3.870033e-05
## clean.data$Deprivation:clean.data$VE 2.145924e-04summary(model2)##
## Call:
## lm(formula = clean.data$RAmy_BA47 ~ clean.data$Deprivation *
## clean.data$VE)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10242 -0.05713 -0.01939 0.04961 0.22930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.103768 0.005771 17.980 < 2e-16
## clean.data$Deprivation 0.020694 0.013462 1.537 0.12600
## clean.data$VE 0.001060 0.012183 0.087 0.93076
## clean.data$Deprivation:clean.data$VE -0.038615 0.014649 -2.636 0.00913
##
## (Intercept) ***
## clean.data$Deprivation
## clean.data$VE
## clean.data$Deprivation:clean.data$VE **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07431 on 179 degrees of freedom
## Multiple R-squared: 0.03983, Adjusted R-squared: 0.02374
## F-statistic: 2.475 on 3 and 179 DF, p-value: 0.06306min(clean.data$VE)## [1] -0.8200771max(clean.data$VE)## [1] 2.169539mean(clean.data$VE)## [1] 0.0384145describe(clean.data$VE)| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
| 1 | 183 | 0.0384 | 0.533 | -0.0283 | -0.0166 | 0.509 | -0.82 | 2.17 | 2.99 | 1.08 | 1.61 | 0.0394 |
max(clean.data$Deprivation)## [1] 2.671064min(clean.data$Deprivation)## [1] -0.7646478mean(clean.data$Deprivation)## [1] 0.02860156describe(clean.data$Deprivation)| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se |
| 1 | 183 | 0.0286 | 0.503 | -0.0469 | -0.0253 | 0.405 | -0.765 | 2.67 | 3.44 | 1.61 | 5.04 | 0.0372 |
Interaction plots/statistics from Preacher Website - http://www.quantpsy.org/interact/mlr2.htm
Your Input
X1 = -0.8200771 X2 = 2.169539 cv1 = -0.47 cv2 = 0.03 cv3 = 0.8 Intercept = 0.103768 X Slope = 0.00106 Z Slope = 0.020694 XZ Slope = -0.038615 df = 166 alpha = 0.05
Asymptotic (Co)variances
var(b0) 0.00003331 var(b1) 0.00014843 var(b2) 0.00018122 var(b3) 0.00021459 cov(b2,b0) 0.00000634 cov(b3,b1) -0.0000387
Region of Significance
Z at lower bound of region = -1.122 Z at upper bound of region = 0.7862 (simple slopes are significant outside this region.)
Simple Intercepts and Slopes at Conditional Values of Z
At Z = cv1… simple intercept = 0.094(0.0082), t=11.4569, p=0 simple slope = 0.0192(0.0152), t=1.2606, p=0.2092 At Z = cv2… simple intercept = 0.1044(0.0058), t=17.9421, p=0 simple slope = -0.0001(0.0121), t=-0.0081, p=0.9935 At Z = cv3… simple intercept = 0.1203(0.0126), t=9.5293, p=0 simple slope = -0.0298(0.015), t=-1.9939, p=0.0478
Simple Intercepts and Slopes at Region Boundaries
Lower Bound…
simple intercept = 0.0805(0.0157), t=5.1231, p=0 simple slope = 0.0444(0.0225), t=1.9743, p=0.05 Upper Bound…
simple intercept = 0.12(0.0125), t=9.6326, p=0 simple slope = -0.0293(0.0148), t=-1.9744, p=0.05
Points to Plot
Line for cv1: From {X=-0.8201, Y=0.0783} to {X=2.1695, Y=0.1357} Line for cv2: From {X=-0.8201, Y=0.1045} to {X=2.1695, Y=0.1042} Line for cv3: From {X=-0.8201, Y=0.1448} to {X=2.1695, Y=0.0556}
xx <- c(-0.8201,2.1695) # <-- change to alter plot dims
yy <- c(0.0378,0.1448) # <-- change to alter plot dims
leg <- c(-0.8201,0.0511) # <-- change to alter legend location
x <- c(-0.8201,2.1695) # <-- x-coords for lines
y1 <- c(0.0783,0.1357)
y2 <- c(0.1045,0.1042)
y3 <- c(0.1448,0.0556)
plot(xx,yy,type='n',font=2,font.lab=2,xlab='X',ylab='Y',main='MLR 2-Way Interaction Plot')
lines(x,y1,lwd=3,lty=1,col=1)
lines(x,y2,lwd=3,lty=5,col=2)
lines(x,y3,lwd=3,lty=6,col=3)
points(x,y1,col=1,pch=16)
points(x,y2,col=1,pch=16)
points(x,y3,col=1,pch=16)
legend(leg[1],leg[2],legend=c('CVz1(1)','CVz1(2)','CVz1(3)'),lwd=c(3,3,3),lty=c(1,5,6),col=c(1,2,3))
z1=-.7646 #supply lower bound for z
z2=2.671 #supply upper bound for z
z <- seq(z1,z2,length=1000)
fz <- c(z,z)
y1 <- (0.00106+-0.038615*z)+(1.9744*sqrt(0.0001484268+(2*z*-0.00003870033)+((z^2)*0.0002145924)))
y2 <- (0.00106+-0.038615*z)-(1.9744*sqrt(0.0001484268+(2*z*-0.00003870033)+((z^2)*0.0002145924)))
fy <- c(y1,y2)
fline <- (0.00106+-0.038615*z)
plot(fz,fy,type='p',pch='.',font=2,font.lab=2,col=2,xlab='Moderator',ylab='Simple Slope',main='Confidence Bands')
lines(z,fline)
f0 <- array(0,c(1000))
lines(z,f0,col=8)
abline(v=-1.122,col=4,lty=2)
abline(v=0.7862,col=4,lty=2)
interact_plot(model, pred = VE, modx = Deprivation, x.label = "Violence Exposure", y.label = "Amygdala-OFC White Matter Connectivity", legend.main = "Social Deprivation",modx.values="plus-minus",plot.points = FALSE, color.class = c("gray54","steelblue4"),line.thickness = 2, rug = TRUE, rug.sides = "bl") + geom_line(linetype=1, size=2) + theme_classic() + theme(text=element_text(size = 16, family="serif")) ## The color.class argument is deprecated. Please use 'colors' instead.
Threat as Moderator
TWO-WAY INTERACTION SIMPLE SLOPES OUTPUTYour Input
X1 = -0.7646 X2 = 2.671 cv1 = -0.46 cv2 = 0.0384145 cv3 = 0.54 Intercept = 0.103768 X Slope = 0.020694 Z Slope = 0.00106 XZ Slope = -0.038615 df = 166 alpha = 0.05
Asymptotic (Co)variances
var(b0) 0.00003331 var(b1) 0.00018122 var(b2) 0.00014843 var(b3) 0.00021459 cov(b2,b0) 0.00000124 cov(b3,b1) -0.00006852
Region of Significance
Z at lower bound of region = -0.2291 Z at upper bound of region = 1.8545 (simple slopes are significant outside this region.)
Simple Intercepts and Slopes at Conditional Values of Z
At Z = cv1… simple intercept = 0.1033(0.008), t=12.953, p=0 simple slope = 0.0385(0.017), t=2.2596, p=0.0251 At Z = cv2… simple intercept = 0.1038(0.0058), t=17.9032, p=0 simple slope = 0.0192(0.0133), t=1.4469, p=0.1498 At Z = cv3… simple intercept = 0.1043(0.0088), t=11.8201, p=0 simple slope = -0.0002(0.013), t=-0.0121, p=0.9903
Simple Intercepts and Slopes at Region Boundaries
Lower Bound…
simple intercept = 0.1035(0.0064), t=16.2613, p=0 simple slope = 0.0295(0.015), t=1.9743, p=0.05 Upper Bound…
simple intercept = 0.1057(0.0234), t=4.5153, p=0 simple slope = -0.0509(0.0258), t=-1.9743, p=0.05
Points to Plot
Line for cv1: From {X=-0.7646, Y=0.0739} to {X=2.671, Y=0.206} Line for cv2: From {X=-0.7646, Y=0.0891} to {X=2.671, Y=0.1551} Line for cv3: From {X=-0.7646, Y=0.1045} to {X=2.671, Y=0.1039}
xx <- c(-0.7646,2.671) # <-- change to alter plot dims
yy <- c(0.0475,0.206) # <-- change to alter plot dims
leg <- c(-0.7646,0.0673) # <-- change to alter legend location
x <- c(-0.7646,2.671) # <-- x-coords for lines
y1 <- c(0.0739,0.206)
y2 <- c(0.0891,0.1551)
y3 <- c(0.1045,0.1039)
plot(xx,yy,type='n',font=2,font.lab=2,xlab='X',ylab='Y',main='MLR 2-Way Interaction Plot')
lines(x,y1,lwd=3,lty=1,col=1)
lines(x,y2,lwd=3,lty=5,col=2)
lines(x,y3,lwd=3,lty=6,col=3)
points(x,y1,col=1,pch=16)
points(x,y2,col=1,pch=16)
points(x,y3,col=1,pch=16)
legend(leg[1],leg[2],legend=c('CVz1(1)','CVz1(2)','CVz1(3)'),lwd=c(3,3,3),lty=c(1,5,6),col=c(1,2,3))
z1=-0.82 #supply lower bound for z
z2=2.17 #supply upper bound for z
z <- seq(z1,z2,length=1000)
fz <- c(z,z)
y1 <- (0.020694+-0.038615*z)+(1.9744*sqrt(0.0001812183+(2*z*-0.00006851916)+((z^2)*0.0002145924)))
y2 <- (0.020694+-0.038615*z)-(1.9744*sqrt(0.0001812183+(2*z*-0.00006851916)+((z^2)*0.0002145924)))
fy <- c(y1,y2)
fline <- (0.020694+-0.038615*z)
plot(fz,fy,type='p',pch='.',font=2,font.lab=2,col=2,xlab='Moderator',ylab='Simple Slope',main='Confidence Bands')
lines(z,fline)
f0 <- array(0,c(1000))
lines(z,f0,col=8)
abline(v=-0.2291,col=4,lty=2)
abline(v=1.8545,col=4,lty=2)
Testing to see if puberty score was higher in parent report.
t.test(clean.data$pubc_mean ~ clean.data$used_pubp)##
## Welch Two Sample t-test
##
## data: clean.data$pubc_mean by clean.data$used_pubp
## t = -0.92773, df = 5.7577, p-value = 0.3908
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.5740279 0.2607511
## sample estimates:
## mean in group 0 mean in group 1
## 3.240584 3.397222Great, they’re not different.
Compare the full sample with the included sample
full_puberty = read_csv('/Users/leighgayle/Box Sync/FF_Demographics/FullSample_Puberty.csv')## Parsed with column specification:
## cols(
## ff_id = col_integer(),
## puberty_score = col_double(),
## used_parent = col_integer(),
## id_short = col_integer()
## )t.test(full_puberty$puberty_score, clean.data$pubc_mean)##
## Welch Two Sample t-test
##
## data: full_puberty$puberty_score and clean.data$pubc_mean
## t = -0.14922, df = 394.16, p-value = 0.8815
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1217439 0.1045666
## sample estimates:
## mean of x mean of y
## 3.237131 3.245719t.test(full_puberty$puberty_score, clean.dataL$pubc_mean)##
## Welch Two Sample t-test
##
## data: full_puberty$puberty_score and clean.dataL$pubc_mean
## t = -0.33286, df = 392.68, p-value = 0.7394
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.13291183 0.09442298
## sample estimates:
## mean of x mean of y
## 3.237131 3.256375full_age = read.csv('/Users/leighgayle/Box Sync/FF_Demographics/Teen.age.csv')
t.test(full_age$Age_Years, clean.data$AgeYears)##
## Welch Two Sample t-test
##
## data: full_age$Age_Years and clean.data$AgeYears
## t = 0.31929, df = 398.04, p-value = 0.7497
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08601324 0.11936908
## sample estimates:
## mean of x mean of y
## 15.86965 15.85297t.test(full_age$Age_Years, clean.dataL$AgeYears)##
## Welch Two Sample t-test
##
## data: full_age$Age_Years and clean.dataL$AgeYears
## t = 0.2993, df = 396.31, p-value = 0.7649
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08753764 0.11897717
## sample estimates:
## mean of x mean of y
## 15.86965 15.85393Making some tables of the non-significant regressions
RAmy_BA10_reg = lm(RAmy_BA10 ~ VE + Deprivation + ThreatDepInt + RAmy_BA47, data = clean.data)
apa.reg.table(RAmy_BA10_reg, filename = "RAmy_BA10_Table.doc", table.number = 5)##
##
## Table 5
##
## Regression results using RAmy_BA10 as the criterion
##
##
## Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI
## (Intercept) 0.03** [0.02, 0.04]
## VE 0.01 [-0.01, 0.02] 0.07 [-0.09, 0.24] .00 [-.01, .02]
## Deprivation 0.00 [-0.01, 0.02] 0.04 [-0.13, 0.21] .00 [-.01, .01]
## ThreatDepInt -0.02 [-0.03, 0.00] -0.16 [-0.32, 0.01] .02 [-.02, .05]
## RAmy_BA47 0.15** [0.07, 0.23] 0.27 [0.13, 0.41] .07 [.00, .14]
##
##
##
## r Fit
##
## .02
## .01
## -.15*
## .30**
## R2 = .107**
## 95% CI[.02,.18]
##
##
## Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
## b represents unstandardized regression weights. beta indicates the standardized regression weights.
## sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
## Square brackets are used to enclose the lower and upper limits of a confidence interval.
## * indicates p < .05. ** indicates p < .01.
## lm.beta(RAmy_BA10_reg)## VE Deprivation ThreatDepInt RAmy_BA47
## 0.07072681 0.03926044 -0.15871285 0.27121381summary(RAmy_BA10_reg)##
## Call:
## lm(formula = RAmy_BA10 ~ VE + Deprivation + ThreatDepInt + RAmy_BA47,
## data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.067036 -0.030903 -0.008152 0.018446 0.120935
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.034716 0.005148 6.743 2.09e-10 ***
## VE 0.005491 0.006488 0.846 0.398551
## Deprivation 0.003235 0.007216 0.448 0.654496
## ThreatDepInt -0.015010 0.007951 -1.888 0.060683 .
## RAmy_BA47 0.149326 0.039804 3.752 0.000238 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03958 on 178 degrees of freedom
## Multiple R-squared: 0.1067, Adjusted R-squared: 0.08667
## F-statistic: 5.318 on 4 and 178 DF, p-value: 0.0004551LAmy_BA10_reg = lm(LAmy_BA10 ~ VE + Deprivation + ThreatDepInt + LAmy_BA11, data = clean.dataL)
lm.beta(LAmy_BA10_reg)## VE Deprivation ThreatDepInt LAmy_BA11
## 0.02637087 0.01286257 -0.06424797 0.61032230summary(LAmy_BA10_reg)##
## Call:
## lm(formula = LAmy_BA10 ~ VE + Deprivation + ThreatDepInt + LAmy_BA11,
## data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.027573 -0.004216 -0.002333 0.000593 0.055848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0030810 0.0012229 2.519 0.0126 *
## VE 0.0007613 0.0020454 0.372 0.7102
## Deprivation 0.0004249 0.0024110 0.176 0.8603
## ThreatDepInt -0.0022878 0.0024140 -0.948 0.3445
## LAmy_BA11 0.3900709 0.0378926 10.294 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01236 on 178 degrees of freedom
## Multiple R-squared: 0.3807, Adjusted R-squared: 0.3668
## F-statistic: 27.35 on 4 and 178 DF, p-value: < 2.2e-16LAmy_BA11_reg = lm(LAmy_BA11 ~ VE + Deprivation + ThreatDepInt + LAmy_BA10, data = clean.dataL)
lm.beta(LAmy_BA11_reg)## VE Deprivation ThreatDepInt LAmy_BA10
## -0.04180465 0.01131408 -0.01831956 0.61143137summary(LAmy_BA11_reg)##
## Call:
## lm(formula = LAmy_BA11 ~ VE + Deprivation + ThreatDepInt + LAmy_BA10,
## data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.046114 -0.009221 -0.005651 0.004268 0.083652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0095433 0.0018130 5.264 4.03e-07 ***
## VE -0.0018883 0.0032013 -0.590 0.556
## Deprivation 0.0005848 0.0037758 0.155 0.877
## ThreatDepInt -0.0010207 0.0037892 -0.269 0.788
## LAmy_BA10 0.9566727 0.0929340 10.294 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01935 on 178 degrees of freedom
## Multiple R-squared: 0.3795, Adjusted R-squared: 0.3656
## F-statistic: 27.22 on 4 and 178 DF, p-value: < 2.2e-16Checking to see how correlated threat and dep are:
cor.test(clean.data$VE, clean.data$Deprivation)##
## Pearson's product-moment correlation
##
## data: clean.data$VE and clean.data$Deprivation
## t = 7.6865, df = 181, p-value = 9.272e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3782395 0.5980962
## sample estimates:
## cor
## 0.4960782fit_cor = lm(RAmy_BA47 ~ VE + Deprivation, data = clean.data)
vif(fit_cor)## VE Deprivation
## 1.326425 1.326425par(family = 'serif')
plot(clean.data$VE, clean.data$Deprivation, frame.plot = FALSE, main=NULL, cex.lab = 1.5, font.lab=2, ylab = "Deprivation", xlab = "Violence Exposure", pch=16)
Analyses not used in final paper.
Incorporation of Psychopathology Data
Note: This is not in the final manuscript.
We’ve got a two factor (anxiety and depression) score and a one factor (internalizing) score created by T.H. that I’ll use.
summary(lm(Internalizing ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + LAmy_BA47, data = clean.data))##
## Call:
## lm(formula = Internalizing ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 +
## RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 +
## LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 +
## LAmy_BA47, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73954 -0.31372 -0.08247 0.27263 1.09336
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06597 0.08908 -0.741 0.460
## RAmy_BA47 0.07627 0.49878 0.153 0.879
## RAmy_BA10 0.16791 1.00735 0.167 0.868
## RAmy_BA11 -0.03244 0.63159 -0.051 0.959
## RAmy_BA24 7.56775 8.76484 0.863 0.389
## RAmy_BA25 0.47272 0.51327 0.921 0.358
## RAmy_BA32 -3.09759 3.48772 -0.888 0.376
## RAmy_BA9 -1.39669 3.86294 -0.362 0.718
## LAmy_BA10 4.09284 2.57346 1.590 0.114
## LAmy_BA11 -1.73328 1.86575 -0.929 0.354
## LAmy_BA9 26.82037 19.12413 1.402 0.163
## LAmy_BA24 -4.21244 6.03390 -0.698 0.486
## LAmy_BA25 0.23894 0.56981 0.419 0.676
## LAmy_BA32 -15.23724 15.37100 -0.991 0.323
## LAmy_BA47 0.64751 1.73676 0.373 0.710
##
## Residual standard error: 0.4248 on 167 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.05372, Adjusted R-squared: -0.02561
## F-statistic: 0.6772 on 14 and 167 DF, p-value: 0.7944summary(lm(Anxiety ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + LAmy_BA47, data = clean.data))##
## Call:
## lm(formula = Anxiety ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + RAmy_BA24 +
## RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + LAmy_BA11 +
## LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + LAmy_BA47,
## data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22937 -0.09263 -0.02237 0.08178 0.34108
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01758 0.02719 -0.647 0.519
## RAmy_BA47 0.01796 0.15222 0.118 0.906
## RAmy_BA10 0.05488 0.30742 0.179 0.859
## RAmy_BA11 -0.01699 0.19275 -0.088 0.930
## RAmy_BA24 2.29422 2.67486 0.858 0.392
## RAmy_BA25 0.14095 0.15664 0.900 0.369
## RAmy_BA32 -0.85373 1.06438 -0.802 0.424
## RAmy_BA9 -0.56197 1.17889 -0.477 0.634
## LAmy_BA10 1.20849 0.78537 1.539 0.126
## LAmy_BA11 -0.49351 0.56939 -0.867 0.387
## LAmy_BA9 8.31291 5.83631 1.424 0.156
## LAmy_BA24 -1.28912 1.84143 -0.700 0.485
## LAmy_BA25 0.05732 0.17389 0.330 0.742
## LAmy_BA32 -4.21521 4.69093 -0.899 0.370
## LAmy_BA47 0.17774 0.53002 0.335 0.738
##
## Residual standard error: 0.1297 on 167 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.05122, Adjusted R-squared: -0.02832
## F-statistic: 0.6439 on 14 and 167 DF, p-value: 0.8251summary(lm(Depression ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 + RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 + LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 + LAmy_BA47, data = clean.data))##
## Call:
## lm(formula = Depression ~ RAmy_BA47 + RAmy_BA10 + RAmy_BA11 +
## RAmy_BA24 + RAmy_BA25 + RAmy_BA32 + RAmy_BA9 + LAmy_BA10 +
## LAmy_BA11 + LAmy_BA9 + LAmy_BA24 + LAmy_BA25 + LAmy_BA32 +
## LAmy_BA47, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73036 -0.31893 -0.08126 0.27474 1.08018
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06907 0.08864 -0.779 0.437
## RAmy_BA47 0.08267 0.49631 0.167 0.868
## RAmy_BA10 0.15963 1.00237 0.159 0.874
## RAmy_BA11 -0.01718 0.62847 -0.027 0.978
## RAmy_BA24 7.53709 8.72147 0.864 0.389
## RAmy_BA25 0.47742 0.51073 0.935 0.351
## RAmy_BA32 -3.16967 3.47046 -0.913 0.362
## RAmy_BA9 -1.25038 3.84382 -0.325 0.745
## LAmy_BA10 4.11192 2.56073 1.606 0.110
## LAmy_BA11 -1.76314 1.85652 -0.950 0.344
## LAmy_BA9 26.42121 19.02950 1.388 0.167
## LAmy_BA24 -4.17902 6.00404 -0.696 0.487
## LAmy_BA25 0.25368 0.56699 0.447 0.655
## LAmy_BA32 -15.53839 15.29494 -1.016 0.311
## LAmy_BA47 0.66632 1.72816 0.386 0.700
##
## Residual standard error: 0.4227 on 167 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.05452, Adjusted R-squared: -0.02474
## F-statistic: 0.6878 on 14 and 167 DF, p-value: 0.7842summary(lm(ADHD_Both_CurrentSx_012 ~ Ramy_0035 , data = data))##
## Call:
## lm(formula = ADHD_Both_CurrentSx_012 ~ Ramy_0035, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.155 -4.146 -2.150 1.850 29.853
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.143216 0.869190 4.767 4.28e-06 ***
## Ramy_0035 0.003958 0.820021 0.005 0.996
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.787 on 155 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 1.503e-07, Adjusted R-squared: -0.006451
## F-statistic: 2.329e-05 on 1 and 155 DF, p-value: 0.9962Plot amygdala associations
plot(clean.data$RAmy_BA47, clean.data$Ramy_0035)
plot(clean.dataL$LAmy_BA10, clean.dataL$Lamy_0035)
plot(data$DepCompc, data$LAmy_BA10)
plot(clean.dataL$DepCompc, clean.dataL$LAmy_BA10)
Relate to Hab Data
cor.test(clean.data$Ramy_Hab_Con0041, clean.data$RAmy_BA47)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_Hab_Con0041 and clean.data$RAmy_BA47
## t = 0.46238, df = 147, p-value = 0.6445
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1234475 0.1976974
## sample estimates:
## cor
## 0.03810896cor.test(clean.data$Ramy_Hab_Con0035, clean.data$RAmy_BA47)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_Hab_Con0035 and clean.data$RAmy_BA47
## t = 0.36895, df = 147, p-value = 0.7127
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1310248 0.1902853
## sample estimates:
## cor
## 0.03041602cor.test(clean.data$Ramy_Hab_Con0027, clean.data$RAmy_BA47)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_Hab_Con0027 and clean.data$RAmy_BA47
## t = 0.3151, df = 147, p-value = 0.7531
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1353849 0.1860035
## sample estimates:
## cor
## 0.02598065Nothing for RAmy_BA47 and Hab.
cor.test(clean.data$Ramy_Hab_Con0041, clean.data$RAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_Hab_Con0041 and clean.data$RAmy_BA10
## t = 1.0666, df = 147, p-value = 0.2879
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07421622 0.24497752
## sample estimates:
## cor
## 0.0876295cor.test(clean.data$Ramy_Hab_Con0035, clean.data$RAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_Hab_Con0035 and clean.data$RAmy_BA10
## t = -0.15172, df = 147, p-value = 0.8796
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1729648 0.1485860
## sample estimates:
## cor
## -0.0125129cor.test(clean.data$Ramy_Hab_Con0027, clean.data$RAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.data$Ramy_Hab_Con0027 and clean.data$RAmy_BA10
## t = 1.5747, df = 147, p-value = 0.1175
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03267615 0.28372427
## sample estimates:
## cor
## 0.1288006RAmy_BA10 is more related to Hab, but still not statistically significant.
cor.test(clean.dataL$Lamy_Hab_Con0041, clean.dataL$LAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.dataL$Lamy_Hab_Con0041 and clean.dataL$LAmy_BA10
## t = 0.33581, df = 148, p-value = 0.7375
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1332578 0.1870274
## sample estimates:
## cor
## 0.02759297cor.test(clean.dataL$Lamy_Hab_Con0035, clean.dataL$LAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.dataL$Lamy_Hab_Con0035 and clean.dataL$LAmy_BA10
## t = -0.098906, df = 148, p-value = 0.9213
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1681722 0.1523303
## sample estimates:
## cor
## -0.008129737cor.test(clean.dataL$Lamy_Hab_Con0027, clean.dataL$LAmy_BA10)##
## Pearson's product-moment correlation
##
## data: clean.dataL$Lamy_Hab_Con0027 and clean.dataL$LAmy_BA10
## t = 0.66792, df = 148, p-value = 0.5052
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1063763 0.2132082
## sample estimates:
## cor
## 0.05481981Nothing for LAmy_BA10 and Hab.
cor.test(clean.dataL$Lamy_Hab_Con0041, clean.dataL$LAmy_BA11)##
## Pearson's product-moment correlation
##
## data: clean.dataL$Lamy_Hab_Con0041 and clean.dataL$LAmy_BA11
## t = 0.48316, df = 148, p-value = 0.6297
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1213489 0.1986823
## sample estimates:
## cor
## 0.03968437cor.test(clean.dataL$Lamy_Hab_Con0035, clean.dataL$LAmy_BA11)##
## Pearson's product-moment correlation
##
## data: clean.dataL$Lamy_Hab_Con0035 and clean.dataL$LAmy_BA11
## t = -0.53628, df = 148, p-value = 0.5926
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2028685 0.1170489
## sample estimates:
## cor
## -0.04403877cor.test(clean.dataL$Lamy_Hab_Con0027, clean.dataL$LAmy_BA11)##
## Pearson's product-moment correlation
##
## data: clean.dataL$Lamy_Hab_Con0027 and clean.dataL$LAmy_BA11
## t = 1.4096, df = 148, p-value = 0.1608
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04601397 0.27037071
## sample estimates:
## cor
## 0.1150963Also nothing for Hab and LAmy_BA11.
Some additional analyses that people may be interested in.
I suppose people will want me to look at all of the potential regions, so here it goes. I just want to document here though that this occurred after my initial exploration of the original significant regions.
# Right Hemisphere
summary(lm(RAmy_BA25 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.data))##
## Call:
## lm(formula = RAmy_BA25 ~ VE * Deprivation + EthnoRace_3cat +
## Gender_1F_2M + Internalizing + pubc_mean, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10324 -0.05575 -0.01503 0.04276 0.20014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.112240 0.057172 1.963 0.0512 .
## VE -0.006036 0.011890 -0.508 0.6123
## Deprivation -0.005611 0.013157 -0.426 0.6703
## EthnoRace_3cat -0.005633 0.007657 -0.736 0.4629
## Gender_1F_2M -0.023729 0.013863 -1.712 0.0887 .
## Internalizing 0.007912 0.013318 0.594 0.5532
## pubc_mean 0.007061 0.011628 0.607 0.5445
## VE:Deprivation 0.006525 0.014078 0.463 0.6436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07092 on 175 degrees of freedom
## Multiple R-squared: 0.0557, Adjusted R-squared: 0.01792
## F-statistic: 1.475 on 7 and 175 DF, p-value: 0.1791summary(lm(RAmy_BA24 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.data))##
## Call:
## lm(formula = RAmy_BA24 ~ VE * Deprivation + EthnoRace_3cat +
## Gender_1F_2M + Internalizing + pubc_mean, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.004167 -0.002211 -0.001269 0.000072 0.035604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0049901 0.0036148 1.380 0.169
## VE 0.0006747 0.0007518 0.898 0.371
## Deprivation 0.0005087 0.0008319 0.611 0.542
## EthnoRace_3cat -0.0001225 0.0004841 -0.253 0.801
## Gender_1F_2M -0.0003722 0.0008765 -0.425 0.672
## Internalizing 0.0002925 0.0008421 0.347 0.729
## pubc_mean -0.0003999 0.0007352 -0.544 0.587
## VE:Deprivation -0.0007434 0.0008901 -0.835 0.405
##
## Residual standard error: 0.004484 on 175 degrees of freedom
## Multiple R-squared: 0.01482, Adjusted R-squared: -0.02458
## F-statistic: 0.3761 on 7 and 175 DF, p-value: 0.9154summary(lm(RAmy_BA11 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.data))##
## Call:
## lm(formula = RAmy_BA11 ~ VE * Deprivation + EthnoRace_3cat +
## Gender_1F_2M + Internalizing + pubc_mean, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08485 -0.05640 -0.01547 0.04440 0.22191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.078831 0.055926 1.410 0.160
## VE -0.015480 0.011631 -1.331 0.185
## Deprivation 0.004850 0.012871 0.377 0.707
## EthnoRace_3cat 0.002774 0.007490 0.370 0.712
## Gender_1F_2M 0.007687 0.013561 0.567 0.572
## Internalizing 0.001974 0.013028 0.151 0.880
## pubc_mean -0.005084 0.011374 -0.447 0.655
## VE:Deprivation -0.011100 0.013772 -0.806 0.421
##
## Residual standard error: 0.06938 on 175 degrees of freedom
## Multiple R-squared: 0.02421, Adjusted R-squared: -0.01482
## F-statistic: 0.6203 on 7 and 175 DF, p-value: 0.7387summary(lm(RAmy_BA32 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.data))##
## Call:
## lm(formula = RAmy_BA32 ~ VE * Deprivation + EthnoRace_3cat +
## Gender_1F_2M + Internalizing + pubc_mean, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.011832 -0.007461 -0.004832 0.000334 0.103865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0101414 0.0125711 0.807 0.421
## VE 0.0026951 0.0026144 1.031 0.304
## Deprivation -0.0005803 0.0028931 -0.201 0.841
## EthnoRace_3cat -0.0006412 0.0016837 -0.381 0.704
## Gender_1F_2M -0.0009671 0.0030483 -0.317 0.751
## Internalizing -0.0020059 0.0029285 -0.685 0.494
## pubc_mean 0.0005674 0.0025567 0.222 0.825
## VE:Deprivation -0.0038126 0.0030956 -1.232 0.220
##
## Residual standard error: 0.01559 on 175 degrees of freedom
## Multiple R-squared: 0.01746, Adjusted R-squared: -0.02184
## F-statistic: 0.4443 on 7 and 175 DF, p-value: 0.873summary(lm(RAmy_BA9 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.data))##
## Call:
## lm(formula = RAmy_BA9 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M +
## Internalizing + pubc_mean, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.014370 -0.007441 -0.005085 0.001558 0.133670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0101125 0.0128203 0.789 0.4313
## VE 0.0055048 0.0026663 2.065 0.0404 *
## Deprivation -0.0019388 0.0029504 -0.657 0.5120
## EthnoRace_3cat -0.0007912 0.0017170 -0.461 0.6455
## Gender_1F_2M -0.0007116 0.0031087 -0.229 0.8192
## Internalizing -0.0005275 0.0029865 -0.177 0.8600
## pubc_mean 0.0005723 0.0026074 0.219 0.8265
## VE:Deprivation -0.0033110 0.0031569 -1.049 0.2957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0159 on 175 degrees of freedom
## Multiple R-squared: 0.02713, Adjusted R-squared: -0.01178
## F-statistic: 0.6973 on 7 and 175 DF, p-value: 0.6743# Left Hemisphere
summary(lm(LAmy_BA25 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.dataL))##
## Call:
## lm(formula = LAmy_BA25 ~ VE * Deprivation + EthnoRace_3cat +
## Gender_1F_2M + Internalizing + pubc_mean, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09234 -0.05060 -0.01075 0.04134 0.20020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.076067 0.051168 1.487 0.139
## VE 0.004024 0.010872 0.370 0.712
## Deprivation -0.002226 0.012621 -0.176 0.860
## EthnoRace_3cat 0.005667 0.006955 0.815 0.416
## Gender_1F_2M -0.012535 0.012555 -0.998 0.319
## Internalizing 0.006765 0.011909 0.568 0.571
## pubc_mean 0.003700 0.010573 0.350 0.727
## VE:Deprivation -0.001429 0.012502 -0.114 0.909
##
## Residual standard error: 0.06372 on 174 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02518, Adjusted R-squared: -0.01404
## F-statistic: 0.642 on 7 and 174 DF, p-value: 0.7207summary(lm(LAmy_BA24 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.dataL))##
## Call:
## lm(formula = LAmy_BA24 ~ VE * Deprivation + EthnoRace_3cat +
## Gender_1F_2M + Internalizing + pubc_mean, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.004792 -0.002642 -0.001426 0.000459 0.039476
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0027783 0.0043438 0.640 0.523
## VE 0.0012933 0.0009229 1.401 0.163
## Deprivation -0.0017135 0.0010714 -1.599 0.112
## EthnoRace_3cat -0.0002864 0.0005904 -0.485 0.628
## Gender_1F_2M -0.0004564 0.0010659 -0.428 0.669
## Internalizing -0.0006608 0.0010110 -0.654 0.514
## pubc_mean 0.0005869 0.0008975 0.654 0.514
## VE:Deprivation -0.0006838 0.0010614 -0.644 0.520
##
## Residual standard error: 0.005409 on 174 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0354, Adjusted R-squared: -0.003406
## F-statistic: 0.9122 on 7 and 174 DF, p-value: 0.4983summary(lm(LAmy_BA47 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.dataL))##
## Call:
## lm(formula = LAmy_BA47 ~ VE * Deprivation + EthnoRace_3cat +
## Gender_1F_2M + Internalizing + pubc_mean, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.027171 -0.016695 -0.009790 0.009257 0.093960
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0204217 0.0203324 1.004 0.317
## VE -0.0046334 0.0043201 -1.073 0.285
## Deprivation 0.0047898 0.0050151 0.955 0.341
## EthnoRace_3cat 0.0008155 0.0027636 0.295 0.768
## Gender_1F_2M 0.0031838 0.0049891 0.638 0.524
## Internalizing 0.0029887 0.0047322 0.632 0.529
## pubc_mean -0.0012774 0.0042012 -0.304 0.761
## VE:Deprivation -0.0014029 0.0049680 -0.282 0.778
##
## Residual standard error: 0.02532 on 174 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.01627, Adjusted R-squared: -0.02331
## F-statistic: 0.411 on 7 and 174 DF, p-value: 0.8946summary(lm(LAmy_BA32 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.dataL))##
## Call:
## lm(formula = LAmy_BA32 ~ VE * Deprivation + EthnoRace_3cat +
## Gender_1F_2M + Internalizing + pubc_mean, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0023513 -0.0012279 -0.0006189 0.0001051 0.0158594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.343e-03 2.034e-03 1.643 0.1021
## VE 8.515e-04 4.322e-04 1.970 0.0504 .
## Deprivation -6.163e-04 5.017e-04 -1.228 0.2210
## EthnoRace_3cat -1.745e-05 2.765e-04 -0.063 0.9497
## Gender_1F_2M -9.220e-04 4.991e-04 -1.847 0.0664 .
## Internalizing -2.466e-04 4.734e-04 -0.521 0.6031
## pubc_mean -1.429e-04 4.203e-04 -0.340 0.7343
## VE:Deprivation -4.826e-04 4.970e-04 -0.971 0.3328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.002533 on 174 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.04921, Adjusted R-squared: 0.01096
## F-statistic: 1.287 on 7 and 174 DF, p-value: 0.2595summary(lm(LAmy_BA9 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M + Internalizing + pubc_mean, data=clean.dataL))##
## Call:
## lm(formula = LAmy_BA9 ~ VE * Deprivation + EthnoRace_3cat + Gender_1F_2M +
## Internalizing + pubc_mean, data = clean.dataL)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.002611 -0.001399 -0.000675 0.000389 0.041338
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0045317 0.0030795 -1.472 0.143
## VE 0.0001276 0.0006543 0.195 0.846
## Deprivation -0.0001965 0.0007596 -0.259 0.796
## EthnoRace_3cat 0.0004712 0.0004186 1.126 0.262
## Gender_1F_2M 0.0010335 0.0007556 1.368 0.173
## Internalizing 0.0005593 0.0007167 0.780 0.436
## pubc_mean 0.0010284 0.0006363 1.616 0.108
## VE:Deprivation -0.0003785 0.0007525 -0.503 0.616
##
## Residual standard error: 0.003835 on 174 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02838, Adjusted R-squared: -0.01071
## F-statistic: 0.726 on 7 and 174 DF, p-value: 0.6502A foray into externalizing disorders
cor.test(clean.data$RAmy_BA47, clean.data$ADHD_Both_CurrentSx_012)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA47 and clean.data$ADHD_Both_CurrentSx_012
## t = 0.60249, df = 181, p-value = 0.5476
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1009741 0.1885707
## sample estimates:
## cor
## 0.04473782cor.test(clean.data$RAmy_BA47, clean.data$ADHD_INT_CurrentSx_012)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA47 and clean.data$ADHD_INT_CurrentSx_012
## t = 0.28697, df = 181, p-value = 0.7745
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1241150 0.1658692
## sample estimates:
## cor
## 0.02132564cor.test(clean.data$RAmy_BA47, clean.data$ADHD_HYP_CurrentSx_012)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA47 and clean.data$ADHD_HYP_CurrentSx_012
## t = 0.87975, df = 181, p-value = 0.3802
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08056734 0.20833660
## sample estimates:
## cor
## 0.0652519cor.test(clean.data$RAmy_BA47, clean.data$ODD_CurrentSx_012)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA47 and clean.data$ODD_CurrentSx_012
## t = -0.10379, df = 181, p-value = 0.9175
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1526001 0.1374963
## sample estimates:
## cor
## -0.007714247summary(lm(ADHD_HYP_CurrentSx_012 ~ VE * Deprivation, data = clean.data))##
## Call:
## lm(formula = ADHD_HYP_CurrentSx_012 ~ VE * Deprivation, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.6237 -1.5579 -0.8720 0.1638 16.6204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5598 0.2516 6.199 3.81e-09 ***
## VE 1.9371 0.5311 3.647 0.000348 ***
## Deprivation -0.4752 0.5869 -0.810 0.419195
## VE:Deprivation -1.3755 0.6387 -2.154 0.032599 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.24 on 179 degrees of freedom
## Multiple R-squared: 0.07852, Adjusted R-squared: 0.06308
## F-statistic: 5.084 on 3 and 179 DF, p-value: 0.002113summary(lm(ADHD_INT_CurrentSx_012 ~ VE * Deprivation, data = clean.data))##
## Call:
## lm(formula = ADHD_INT_CurrentSx_012 ~ VE * Deprivation, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8458 -2.7717 -2.1251 0.8705 15.2486
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.9170 0.3555 8.204 4.4e-14 ***
## VE 0.5163 0.7506 0.688 0.492
## Deprivation -0.4611 0.8293 -0.556 0.579
## VE:Deprivation -0.6568 0.9025 -0.728 0.468
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.578 on 179 degrees of freedom
## Multiple R-squared: 0.007162, Adjusted R-squared: -0.009478
## F-statistic: 0.4304 on 3 and 179 DF, p-value: 0.7315summary(lm(ADHD_Both_CurrentSx_012 ~ VE * Deprivation, data = clean.data))##
## Call:
## lm(formula = ADHD_Both_CurrentSx_012 ~ VE * Deprivation, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.4694 -3.7747 -2.5809 0.8904 30.7562
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4768 0.5436 8.236 3.63e-14 ***
## VE 2.4534 1.1475 2.138 0.0339 *
## Deprivation -0.9363 1.2679 -0.738 0.4612
## VE:Deprivation -2.0323 1.3797 -1.473 0.1425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.999 on 179 degrees of freedom
## Multiple R-squared: 0.03171, Adjusted R-squared: 0.01548
## F-statistic: 1.954 on 3 and 179 DF, p-value: 0.1226summary(lm(ODD_CurrentSx_012 ~ VE * Deprivation, data = clean.data))##
## Call:
## lm(formula = ODD_CurrentSx_012 ~ VE * Deprivation, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6158 -1.5080 -1.1038 -0.1268 12.4333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.472820 0.217050 6.786 1.63e-10 ***
## VE 0.857791 0.458196 1.872 0.0628 .
## Deprivation -0.005558 0.506286 -0.011 0.9913
## VE:Deprivation -0.677161 0.550937 -1.229 0.2206
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.795 on 179 degrees of freedom
## Multiple R-squared: 0.02387, Adjusted R-squared: 0.00751
## F-statistic: 1.459 on 3 and 179 DF, p-value: 0.2273summary(lm(ADHD_HYP_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data=clean.data))##
## Call:
## lm(formula = ADHD_HYP_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 +
## Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2493 -1.7059 -0.8878 -0.1826 17.1588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8952 0.8451 2.243 0.0262 *
## RAmy_BA47 3.1544 3.4517 0.914 0.3620
## RAmy_BA10 -2.5025 6.3840 -0.392 0.6955
## Gender_0F_1M 0.9295 0.4967 1.872 0.0629 .
## EthnoRace_C -1.7542 0.9487 -1.849 0.0661 .
## EthnoRace_AA -1.1475 0.7478 -1.535 0.1267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.309 on 177 degrees of freedom
## Multiple R-squared: 0.04963, Adjusted R-squared: 0.02279
## F-statistic: 1.849 on 5 and 177 DF, p-value: 0.1057summary(lm(ADHD_INT_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data=clean.data))##
## Call:
## lm(formula = ADHD_INT_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 +
## Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1019 -2.6464 -1.5444 0.7104 14.8818
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.12540 1.14822 1.851 0.0658 .
## RAmy_BA47 -1.64082 4.68988 -0.350 0.7269
## RAmy_BA10 14.75289 8.67398 1.701 0.0907 .
## Gender_0F_1M 1.63418 0.67481 2.422 0.0165 *
## EthnoRace_C 0.05433 1.28907 0.042 0.9664
## EthnoRace_AA -0.82420 1.01601 -0.811 0.4183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.496 on 177 degrees of freedom
## Multiple R-squared: 0.05336, Adjusted R-squared: 0.02662
## F-statistic: 1.995 on 5 and 177 DF, p-value: 0.08148summary(lm(ADHD_Both_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data=clean.data))##
## Call:
## lm(formula = ADHD_Both_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 +
## Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.2173 -3.5825 -2.3233 0.1034 29.9354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.021 1.779 2.261 0.0250 *
## RAmy_BA47 1.514 7.265 0.208 0.8352
## RAmy_BA10 12.250 13.436 0.912 0.3631
## Gender_0F_1M 2.564 1.045 2.453 0.0152 *
## EthnoRace_C -1.700 1.997 -0.851 0.3957
## EthnoRace_AA -1.972 1.574 -1.253 0.2119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.964 on 177 degrees of freedom
## Multiple R-squared: 0.05217, Adjusted R-squared: 0.02539
## F-statistic: 1.948 on 5 and 177 DF, p-value: 0.08861summary(lm(ODD_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M + EthnoRace_C + EthnoRace_AA, data=clean.data))##
## Call:
## lm(formula = ODD_CurrentSx_012 ~ RAmy_BA47 + RAmy_BA10 + Gender_0F_1M +
## EthnoRace_C + EthnoRace_AA, data = clean.data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9031 -1.4834 -1.2628 -0.1831 12.7625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.6661 0.7240 2.301 0.0225 *
## RAmy_BA47 0.2990 2.9570 0.101 0.9196
## RAmy_BA10 -3.2690 5.4691 -0.598 0.5508
## Gender_0F_1M 0.2240 0.4255 0.526 0.5992
## EthnoRace_C -0.6228 0.8128 -0.766 0.4446
## EthnoRace_AA -0.1922 0.6406 -0.300 0.7645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.835 on 177 degrees of freedom
## Multiple R-squared: 0.007149, Adjusted R-squared: -0.0209
## F-statistic: 0.2549 on 5 and 177 DF, p-value: 0.9369cor.test(clean.data$RAmy_BA10, clean.data$ADHD_Both_CurrentSx_012)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA10 and clean.data$ADHD_Both_CurrentSx_012
## t = 0.92146, df = 181, p-value = 0.358
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07749358 0.21129351
## sample estimates:
## cor
## 0.06833117cor.test(clean.data$RAmy_BA10, clean.data$ADHD_INT_CurrentSx_012)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA10 and clean.data$ADHD_INT_CurrentSx_012
## t = 1.4772, df = 181, p-value = 0.1414
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03648892 0.25024021
## sample estimates:
## cor
## 0.1091454cor.test(clean.data$RAmy_BA10, clean.data$ADHD_HYP_CurrentSx_012)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA10 and clean.data$ADHD_HYP_CurrentSx_012
## t = -0.061647, df = 181, p-value = 0.9509
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1495394 0.1405679
## sample estimates:
## cor
## -0.004582154cor.test(clean.data$RAmy_BA10, clean.data$ODD_CurrentSx_012)##
## Pearson's product-moment correlation
##
## data: clean.data$RAmy_BA10 and clean.data$ODD_CurrentSx_012
## t = -0.50025, df = 181, p-value = 0.6175
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1812371 0.1084840
## sample estimates:
## cor
## -0.03715734Ensuring that the effects are not due to medications.
Subjects 10003, 10023, 10154, and 10184 are on antipsychotics or anticonvulsants. I think we’ll exclude these folks and see what we’ve got.
no_antipsych_data = clean.data[-c(3,22,126,150), ]summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean, data=no_antipsych_data))##
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean, data = no_antipsych_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12882 -0.05300 -0.01711 0.05242 0.21397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.072599 0.046072 1.576 0.11695
## VE 0.004606 0.012326 0.374 0.70910
## Deprivation 0.018697 0.013724 1.362 0.17492
## EthnoRace_C 0.016371 0.021456 0.763 0.44652
## EthnoRace_AA -0.019830 0.016895 -1.174 0.24216
## Gender_0F_1M 0.005553 0.014295 0.388 0.69816
## RAmy_BA10 0.527048 0.139088 3.789 0.00021 ***
## Internalizing 0.008377 0.013934 0.601 0.54853
## pubc_mean 0.004571 0.012086 0.378 0.70572
## VE:Deprivation -0.035318 0.015036 -2.349 0.01999 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07213 on 169 degrees of freedom
## Multiple R-squared: 0.1282, Adjusted R-squared: 0.08182
## F-statistic: 2.762 on 9 and 169 DF, p-value: 0.00486cor.test(no_antipsych_data$RAmy_BA47, no_antipsych_data$ThreatCompc)##
## Pearson's product-moment correlation
##
## data: no_antipsych_data$RAmy_BA47 and no_antipsych_data$ThreatCompc
## t = -0.25291, df = 177, p-value = 0.8006
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1652183 0.1280226
## sample estimates:
## cor
## -0.01900655cor.test(no_antipsych_data$RAmy_BA47, no_antipsych_data$DepCompc)##
## Pearson's product-moment correlation
##
## data: no_antipsych_data$RAmy_BA47 and no_antipsych_data$DepCompc
## t = 0.29686, df = 177, p-value = 0.7669
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1247727 0.1684290
## sample estimates:
## cor
## 0.02230779summary(lm(Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, data = no_antipsych_data))##
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean,
## data = no_antipsych_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.56282 -0.37431 0.02869 0.35142 1.93076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.21644 0.30664 3.967 0.000114 ***
## RAmy_BA47 -2.75789 0.70081 -3.935 0.000128 ***
## Internalizing 0.17906 0.12654 1.415 0.159207
## pubc_mean -0.03204 0.09120 -0.351 0.725856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6444 on 145 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.1046, Adjusted R-squared: 0.0861
## F-statistic: 5.648 on 3 and 145 DF, p-value: 0.001093summary(lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean, data=no_antipsych_data))##
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean, data = no_antipsych_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05744 -0.02956 -0.00912 0.01785 0.11939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.522e-02 2.456e-02 1.027 0.30602
## VE 1.165e-03 6.546e-03 0.178 0.85892
## Deprivation 7.047e-05 7.327e-03 0.010 0.99234
## EthnoRace_C -7.302e-03 1.140e-02 -0.641 0.52263
## EthnoRace_AA 1.579e-02 8.925e-03 1.769 0.07867 .
## Gender_0F_1M 1.803e-04 7.593e-03 0.024 0.98108
## RAmy_BA47 1.486e-01 3.921e-02 3.789 0.00021 ***
## Internalizing 3.354e-03 7.402e-03 0.453 0.65107
## pubc_mean -8.503e-04 6.419e-03 -0.132 0.89478
## VE:Deprivation -8.305e-03 8.088e-03 -1.027 0.30594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0383 on 169 degrees of freedom
## Multiple R-squared: 0.1408, Adjusted R-squared: 0.09509
## F-statistic: 3.078 on 9 and 169 DF, p-value: 0.001912cor.test(no_antipsych_data$RAmy_BA10, no_antipsych_data$ThreatCompc)##
## Pearson's product-moment correlation
##
## data: no_antipsych_data$RAmy_BA10 and no_antipsych_data$ThreatCompc
## t = 0.2451, df = 177, p-value = 0.8067
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1285999 0.1646473
## sample estimates:
## cor
## 0.01841986cor.test(no_antipsych_data$RAmy_BA10, no_antipsych_data$DepCompc)##
## Pearson's product-moment correlation
##
## data: no_antipsych_data$RAmy_BA10 and no_antipsych_data$DepCompc
## t = 0.068653, df = 177, p-value = 0.9453
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1416193 0.1517176
## sample estimates:
## cor
## 0.005160188summary(lm(Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, data = no_antipsych_data))##
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean,
## data = no_antipsych_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.88289 -0.33502 0.00632 0.33461 1.93854
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.14418 0.31406 3.643 0.000374 ***
## RAmy_BA10 -3.80083 1.34663 -2.822 0.005435 **
## Internalizing 0.15940 0.12942 1.232 0.220066
## pubc_mean -0.04300 0.09345 -0.460 0.646067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.66 on 145 degrees of freedom
## (30 observations deleted due to missingness)
## Multiple R-squared: 0.06061, Adjusted R-squared: 0.04117
## F-statistic: 3.118 on 3 and 145 DF, p-value: 0.02804no_anxiety_dep_med_data = clean.data[-c(3,22,126,138,150,162), ]summary(lm(RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean, data=no_anxiety_dep_med_data))##
## Call:
## lm(formula = RAmy_BA47 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + RAmy_BA10 + Internalizing + pubc_mean, data = no_anxiety_dep_med_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.13009 -0.05361 -0.01864 0.05364 0.21577
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065121 0.046831 1.391 0.166215
## VE 0.004133 0.012406 0.333 0.739476
## Deprivation 0.021172 0.014104 1.501 0.135211
## EthnoRace_C 0.015304 0.021549 0.710 0.478553
## EthnoRace_AA -0.019621 0.016959 -1.157 0.248947
## Gender_0F_1M 0.008194 0.014602 0.561 0.575431
## RAmy_BA10 0.514025 0.140124 3.668 0.000328 ***
## Internalizing 0.009809 0.014060 0.698 0.486331
## pubc_mean 0.006775 0.012330 0.549 0.583443
## VE:Deprivation -0.034307 0.015116 -2.270 0.024513 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07235 on 167 degrees of freedom
## Multiple R-squared: 0.1255, Adjusted R-squared: 0.07833
## F-statistic: 2.662 on 9 and 167 DF, p-value: 0.006544cor.test(no_anxiety_dep_med_data$RAmy_BA47, no_anxiety_dep_med_data$ThreatCompc)##
## Pearson's product-moment correlation
##
## data: no_anxiety_dep_med_data$RAmy_BA47 and no_anxiety_dep_med_data$ThreatCompc
## t = -0.14213, df = 175, p-value = 0.8871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1579939 0.1369740
## sample estimates:
## cor
## -0.01074368cor.test(no_anxiety_dep_med_data$RAmy_BA47,no_anxiety_dep_med_data$DepCompc)##
## Pearson's product-moment correlation
##
## data: no_anxiety_dep_med_data$RAmy_BA47 and no_anxiety_dep_med_data$DepCompc
## t = 0.5774, df = 175, p-value = 0.5644
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1045676 0.1898849
## sample estimates:
## cor
## 0.04360558summary(lm(Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean, data =no_anxiety_dep_med_data))##
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA47 + Internalizing + pubc_mean,
## data = no_anxiety_dep_med_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.56456 -0.38299 0.03202 0.31050 1.93533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.23256 0.30843 3.996 0.000102 ***
## RAmy_BA47 -2.74381 0.70270 -3.905 0.000145 ***
## Internalizing 0.18138 0.12687 1.430 0.154989
## pubc_mean -0.03832 0.09197 -0.417 0.677534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6458 on 144 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1045, Adjusted R-squared: 0.08587
## F-statistic: 5.603 on 3 and 144 DF, p-value: 0.00116summary(lm(RAmy_BA10 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA + Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean, data=no_anxiety_dep_med_data))##
## Call:
## lm(formula = RAmy_BA10 ~ VE * Deprivation + EthnoRace_C + EthnoRace_AA +
## Gender_0F_1M + RAmy_BA47 + Internalizing + pubc_mean, data = no_anxiety_dep_med_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.05728 -0.03001 -0.00735 0.01807 0.11962
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0216889 0.0249664 0.869 0.386246
## VE 0.0009942 0.0065925 0.151 0.880305
## Deprivation 0.0012091 0.0075425 0.160 0.872834
## EthnoRace_C -0.0077174 0.0114494 -0.674 0.501216
## EthnoRace_AA 0.0157903 0.0089628 1.762 0.079938 .
## Gender_0F_1M 0.0014130 0.0077639 0.182 0.855809
## RAmy_BA47 0.1450724 0.0395470 3.668 0.000328 ***
## Internalizing 0.0040167 0.0074736 0.537 0.591669
## pubc_mean 0.0002370 0.0065565 0.036 0.971214
## VE:Deprivation -0.0078888 0.0081305 -0.970 0.333310
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03844 on 167 degrees of freedom
## Multiple R-squared: 0.1386, Adjusted R-squared: 0.09219
## F-statistic: 2.986 on 9 and 167 DF, p-value: 0.002529cor.test(no_anxiety_dep_med_data$RAmy_BA10, no_anxiety_dep_med_data$ThreatCompc)##
## Pearson's product-moment correlation
##
## data: no_anxiety_dep_med_data$RAmy_BA10 and no_anxiety_dep_med_data$ThreatCompc
## t = 0.34737, df = 175, p-value = 0.7287
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1217225 0.1730799
## sample estimates:
## cor
## 0.0262494cor.test(no_anxiety_dep_med_data$RAmy_BA10,no_anxiety_dep_med_data$DepCompc)##
## Pearson's product-moment correlation
##
## data: no_anxiety_dep_med_data$RAmy_BA10 and no_anxiety_dep_med_data$DepCompc
## t = 0.33453, df = 175, p-value = 0.7384
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1226782 0.1721386
## sample estimates:
## cor
## 0.02527989summary(lm(Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean, data =no_anxiety_dep_med_data))##
## Call:
## lm(formula = Ramy_0035 ~ RAmy_BA10 + Internalizing + pubc_mean,
## data = no_anxiety_dep_med_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.88318 -0.33528 0.00011 0.33462 1.94332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.16411 0.31591 3.685 0.000323 ***
## RAmy_BA10 -3.79210 1.34908 -2.811 0.005629 **
## Internalizing 0.16227 0.12971 1.251 0.212968
## pubc_mean -0.05028 0.09419 -0.534 0.594294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6612 on 144 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.06122, Adjusted R-squared: 0.04167
## F-statistic: 3.13 on 3 and 144 DF, p-value: 0.02762It doesn’t seem like the medication for anxiety/depression/antipsychotics influence our effect, which is good.
Potentially look at mediation with the interaction, RAmy_BA47, and ramy activation.
I’d like to note here that I don’t think we really have the sample size to do this, but I guess it would be worth a shot.
#library(lavaan)
#
# hayes7 <- ' # regressions
# RAmy_BA47 ~ a1*ThreatCompc
# Ramy_0035 ~ b1*RAmy_BA47
# RAmy_BA47 ~ a2*DepCompc
# RAmy_BA47 ~ a3*ThreatDepInt
# Ramy_0035 ~ cdash*ThreatCompc
# # mean of centered write (for use in simple slopes)
# DepCompc ~ DepCompc.mean*1
# # variance of centered write (for use in simple slopes)
# DepCompc ~~ DepCompc.var*DepCompc
# # indirect effects conditional on moderator (a1 + a3*a2.value)*b1
# indirect.SDbelow := a1*b1 + a3*-sqrt(DepCompc.var)*b1
# indirect.mean := a1*b1 + a3*DepCompc.mean*b1
# indirect.SDabove := a1*b1 + a3*(sqrt(DepCompc.var)*1.5)*b1'# fit model
#sem <- sem(model = hayes7, data = clean.data,
#se = "bootstrap", bootstrap = 1000)# fit measures
#summary(sem,
#fit.measures = TRUE,
#standardize = TRUE, rsquare = TRUE)So I think this is similar to where we ended up with M.P. and the MPlus expedition of a while ago. I just don’t think we have the power to detect this moderated mediation and it’s probably because of the make-up of the sample as well in terms of who is both high in social dep and violence exposure, but I’m not entirely sure that I’m doing this correctly.
#DEMONSTRATE HOW POWER A PRIORI
#GENERATING A POPULATION
library(stats)
n=100
reps=1000
sims=NULL
for(m in 1:reps)
{
x=rnorm(n,0,1)
y=.3*x+rnorm(n,0,sqrt(1-.3^2))
l=lm(y~x)
cor=cor(x,y)
slope=summary(l)$coefficients[2,1]
se=summary(l)$coefficients[2,2]
p=summary(l )$coefficients[2,4]
sig=ifelse(p<.05,1,0)
est=cbind(slope,cor,se,p,sig)
sims=rbind(sims,est)
}
fix(sims)
sampling.dist=data.frame(sims)
sampling.dist$slope## [1] 0.293174669 0.228791139 0.181371311 0.200154006 0.325103479
## [6] 0.545297849 0.228792554 0.413846808 0.319241276 0.252866775
## [11] 0.535881117 0.280909099 0.245258492 0.417219357 0.275737112
## [16] 0.347642886 0.479719921 0.219622261 0.397274380 0.417306904
## [21] 0.343832587 0.100312121 0.237986757 0.283780253 0.273040595
## [26] 0.302602275 0.252448422 0.189240527 0.243232108 0.260281149
## [31] 0.327207413 0.305637786 0.174280370 0.336252982 0.134163795
## [36] 0.303996915 0.370145789 0.404442927 0.335859644 0.211981149
## [41] 0.215842949 0.356378280 0.367775384 0.267863635 0.392496704
## [46] 0.350062437 0.268586029 0.368477207 0.084040153 0.280462612
## [51] 0.350616694 0.315015327 0.233330269 0.270816864 0.257032971
## [56] 0.276956200 0.276259667 0.235475744 0.370631400 0.240952308
## [61] 0.263015637 0.375729804 0.366816724 0.317664838 0.368692793
## [66] 0.277762058 0.359369586 0.222142283 0.273928510 0.363286018
## [71] 0.297681438 0.318607401 0.222938674 0.375585222 0.270670215
## [76] 0.286065609 0.261333708 0.445572834 0.351231095 0.355092680
## [81] 0.349995470 0.217468591 0.313195806 0.276015957 0.314507705
## [86] 0.572600739 0.355287310 0.478993406 0.020498129 0.281464436
## [91] 0.296723318 0.325770699 0.497606129 0.278552329 0.319792223
## [96] 0.406495694 0.093296714 0.394529418 0.316021436 0.366255352
## [101] 0.381317569 0.288251899 0.435488979 0.143500040 0.180242212
## [106] 0.413234285 0.062373794 0.363685515 0.175417815 0.182252320
## [111] 0.267105175 0.178265644 0.222870182 0.315918981 0.219145314
## [116] 0.409346169 0.272089440 0.399298606 0.376678754 0.226738713
## [121] 0.159484592 0.341037820 0.269951897 0.156235468 0.276456539
## [126] 0.093764845 0.234406280 0.339662368 0.314120367 0.133979766
## [131] 0.334976694 0.262605450 0.272669603 0.315829699 0.196589963
## [136] 0.303621526 0.398268033 0.263577379 0.296556729 0.321439194
## [141] 0.358038485 0.415362331 0.488602221 0.275076945 0.327667563
## [146] 0.178357743 0.369290862 0.177165709 0.276743518 0.172924533
## [151] 0.488909744 0.147825645 0.298455845 0.296155808 0.108435713
## [156] 0.334636735 0.300111495 0.343942264 0.548592067 0.402458207
## [161] 0.266459642 0.432587652 0.279216382 0.330143083 0.288988121
## [166] 0.267146769 0.147206898 0.312746843 0.352745048 0.186379070
## [171] 0.281493706 0.227071950 0.392893837 0.267598282 0.386595736
## [176] 0.387268815 0.301399776 0.339670075 0.346391065 0.234473121
## [181] 0.386800437 0.169533565 0.374876401 0.257886722 0.258420140
## [186] 0.257241295 0.292423879 0.369247162 0.315624047 0.345277671
## [191] 0.298695802 0.375100047 0.308026435 0.278371859 0.231118677
## [196] 0.441146790 0.076094004 0.280867532 0.168814117 0.529558406
## [201] 0.367568009 0.317408382 0.391746681 0.306285125 0.336559901
## [206] 0.408244110 0.296713299 0.206567021 0.212293029 0.214380723
## [211] 0.303031504 0.235098188 0.316863029 0.218300232 0.279698196
## [216] 0.218928318 0.247319045 0.230890375 0.557191304 0.487358942
## [221] 0.285906534 0.224607901 0.340265075 0.419895877 0.282477371
## [226] 0.365933080 0.384435017 0.384012413 0.346825247 0.199819417
## [231] 0.354732372 0.258668654 0.289614246 0.370109469 0.283902175
## [236] 0.141542822 0.424728465 0.442134379 0.230227462 0.380491793
## [241] 0.277795578 0.343658253 0.286529285 0.150784426 0.233952269
## [246] 0.206666487 0.386356427 0.377821251 0.295073110 0.331892666
## [251] 0.476919847 0.282250820 0.207772354 0.350412688 0.272319667
## [256] 0.349962975 0.327796741 0.367721046 0.257229856 0.335053670
## [261] 0.180913268 0.331359653 0.277365913 0.233516212 0.325899825
## [266] 0.367142657 0.198471453 0.461623832 0.374150000 0.228016253
## [271] 0.450756996 0.264937053 0.362729750 0.213845667 0.248645441
## [276] 0.338565592 0.413826713 0.278445692 0.242586415 0.368444747
## [281] 0.145288929 0.304695595 0.518593245 0.075123194 0.276472601
## [286] 0.256885476 0.195507534 0.230998113 0.464973981 0.299697735
## [291] 0.399887285 0.204490973 0.483565651 0.269177712 0.281508923
## [296] 0.198231168 0.237539266 0.354062895 0.334502749 0.272029706
## [301] 0.470994161 0.156711532 0.183281137 0.320839865 0.330873402
## [306] 0.324329745 0.228674744 0.245329662 0.359231828 0.200812369
## [311] 0.301806669 0.195752662 0.199328130 0.548880056 0.189728841
## [316] 0.192756046 0.296640444 0.315865380 0.204602309 0.254437723
## [321] 0.399230220 0.371455487 0.174224136 0.356176415 0.336050082
## [326] 0.379350538 0.386603045 0.407874965 0.408781869 0.140019424
## [331] 0.206712759 0.456518145 0.309574771 0.248849206 0.258753610
## [336] 0.208117221 0.112259339 0.410864575 0.442122099 0.462173941
## [341] 0.182186569 0.199178292 0.395941315 0.091285743 0.354499461
## [346] 0.320784968 0.208992588 0.237112812 0.387474070 0.419392731
## [351] 0.429115797 0.304722448 0.132033910 0.347093751 0.371574155
## [356] 0.252861435 0.428739817 0.239836088 0.261577242 0.273912251
## [361] 0.334062304 0.229597033 0.333222563 0.664307913 0.388957170
## [366] 0.411653558 0.192641594 0.279719229 0.177016186 0.255924154
## [371] 0.185762203 0.352232305 0.415513280 0.424946436 0.463556545
## [376] 0.253907736 0.356428925 0.196691380 0.293388841 0.235359012
## [381] 0.317266306 0.360376346 0.099687368 0.261135327 0.331746583
## [386] 0.349948971 0.409509058 0.405689379 0.321073297 0.296614324
## [391] 0.356221897 0.495592043 0.230775158 0.405024325 0.519037580
## [396] 0.259754323 0.412321651 0.338207482 0.310904060 0.248711457
## [401] 0.382473407 0.121154895 0.061854728 0.420811510 0.221894745
## [406] 0.355189397 0.368940520 0.310677166 0.122006683 0.355264293
## [411] 0.441959105 0.234060588 0.279136759 0.169322541 0.218905662
## [416] 0.360775817 0.227335029 0.255940830 0.308046619 0.445729563
## [421] 0.212887349 0.387208071 0.444771230 0.263234622 0.322229317
## [426] 0.189066322 0.446574956 0.182804803 0.305831845 0.319366964
## [431] 0.367806764 0.619107120 0.166507403 0.241217973 0.509297341
## [436] 0.180085752 0.299164814 0.275488186 0.057796736 0.221446658
## [441] 0.148821163 0.519485194 0.279388590 0.254087109 0.436539404
## [446] 0.400875745 0.279380387 0.278866245 0.092707000 0.364918573
## [451] 0.337274355 0.337784360 0.322448557 0.338224258 0.322802526
## [456] 0.438582604 0.354830357 0.410731613 0.258734397 0.001356847
## [461] 0.228825845 0.223535682 0.495500438 0.325464099 0.301721730
## [466] 0.315040135 0.302627506 0.285142131 0.507283513 0.251693555
## [471] 0.156953833 0.278258716 0.507431223 0.223615199 0.362991086
## [476] 0.347602802 0.321666946 0.335551076 0.260756869 0.352106557
## [481] 0.335848957 0.425406523 0.314113289 0.201264867 0.335183693
## [486] 0.122483595 0.345817418 0.462033174 0.326638740 0.301641370
## [491] 0.266154745 0.199295362 0.241326559 0.238798755 0.365784514
## [496] 0.171291978 0.529433075 0.302962918 0.192852383 0.161732478
## [501] 0.405467206 0.287073683 0.356828858 0.214235042 0.267106721
## [506] 0.294277749 0.262328909 0.403425069 0.330201432 0.342752167
## [511] 0.362151225 0.416553102 0.282662429 0.435349554 0.270012199
## [516] 0.351503268 0.439813260 0.251939995 0.188747711 0.395880603
## [521] 0.205928588 0.260737979 0.488874246 0.293668046 0.197756621
## [526] 0.275776802 0.243604954 0.298005991 0.148350748 0.338020174
## [531] 0.304368112 0.260608482 0.333352437 0.339760610 0.561426475
## [536] 0.384950395 0.428559347 0.367917795 0.339791428 0.187251400
## [541] 0.202097865 0.276684352 0.467635839 0.419208423 0.401455662
## [546] 0.262197544 0.222703036 0.191101709 0.385442172 0.323579758
## [551] 0.541432071 0.330510584 0.238614021 0.178515934 0.280571871
## [556] 0.421858709 0.243489416 0.355753827 0.335440154 0.350961110
## [561] 0.165577180 0.290260879 0.195946925 0.426553748 0.243420184
## [566] 0.294792925 0.532381984 0.189134537 0.351236479 0.509691338
## [571] 0.365619764 0.235202700 0.470892893 0.309085870 0.141993455
## [576] 0.265061485 0.359312634 0.393261202 0.203887788 0.292990375
## [581] 0.303052647 0.058819207 0.402056491 0.373838374 0.169497105
## [586] 0.277091495 0.384580153 0.478294779 0.326845135 0.210342048
## [591] 0.140367961 0.286023588 0.495650539 0.347888721 0.264384035
## [596] 0.358433368 0.230443433 0.260991444 0.390231519 0.333024247
## [601] 0.209883179 0.223392785 0.350395871 0.378567110 0.134210219
## [606] 0.249383317 0.439363984 0.309180603 0.364809286 0.220355079
## [611] 0.171062101 0.209889211 0.237473955 0.238976345 0.219705757
## [616] 0.176464822 0.412874942 0.437797610 0.379638482 0.167654702
## [621] 0.563985794 0.328533108 0.404247530 0.168449192 0.387233925
## [626] 0.148378497 0.157355899 0.330613850 0.339495085 0.310571463
## [631] 0.408094854 0.251779200 0.360770232 0.403097418 0.327501814
## [636] 0.085967995 0.320504105 0.313233710 0.233302554 0.319539162
## [641] 0.225278641 0.285958127 0.305820826 0.377097571 0.371286276
## [646] 0.413969832 0.311253408 0.439781274 0.257986263 0.327142174
## [651] 0.248583415 0.387181636 0.306020807 0.200570079 0.191751490
## [656] 0.351476024 0.266585496 0.208187964 0.238572213 0.327147800
## [661] 0.208478538 0.421072778 0.238599815 0.390119010 0.176823939
## [666] 0.375142229 0.227591214 0.244004310 0.038698204 0.196145751
## [671] 0.298804546 0.361858605 0.378098717 0.245834261 0.280992500
## [676] 0.233917103 0.071081077 0.335319525 0.316522562 0.167389750
## [681] 0.139692795 0.370340059 0.245629902 0.157160492 0.359139245
## [686] 0.574751170 0.293648350 0.235983910 0.166876572 0.344343314
## [691] 0.234644325 0.392319674 0.252549550 0.395700890 0.493845376
## [696] 0.303643228 0.482070752 0.402821801 0.440754666 0.400645475
## [701] 0.273958151 0.183346005 0.497081481 0.437078208 0.395806570
## [706] 0.330637070 0.194499792 0.475213398 0.237199203 0.331291942
## [711] 0.404510600 0.377770776 0.081424822 0.327186114 0.131358504
## [716] 0.357085133 0.355933124 0.283908514 0.218739548 0.409592539
## [721] 0.194632533 0.079806027 0.200861219 0.249933116 0.343914269
## [726] 0.231717723 0.082895142 0.209820335 0.424053122 0.501978448
## [731] 0.357917970 0.348766217 0.299269803 0.426109434 0.138391848
## [736] 0.372748392 0.290378179 0.424905729 0.508413718 0.191985842
## [741] 0.393631515 0.100325257 0.355182045 0.387853595 0.335809224
## [746] 0.309705883 0.346110287 0.145738775 0.375694952 0.392933544
## [751] 0.371470171 0.431747735 0.407442258 0.426756503 0.311522673
## [756] 0.380661017 0.503537792 0.326861868 0.329455346 0.276332111
## [761] 0.380424742 0.364116792 0.281388592 0.263977971 0.445064493
## [766] 0.132038291 0.210135576 0.322482831 0.490692455 0.259802182
## [771] 0.164725016 0.423858770 0.137530422 0.383927214 0.315147491
## [776] 0.450960817 0.222154847 0.209776749 0.337724916 0.250355186
## [781] 0.220319139 0.243262642 0.232957385 0.395106469 0.139929798
## [786] 0.241220683 0.287918580 0.271305016 0.342277743 0.342735272
## [791] 0.235474062 0.319849365 0.226286658 0.310987263 0.282245925
## [796] 0.268291899 0.240483206 0.324347120 0.386060143 0.089822077
## [801] 0.250563223 0.182414120 0.412966805 0.176337929 0.143222578
## [806] 0.304570116 0.236856448 0.514765539 0.422478858 0.318539853
## [811] 0.313918929 0.093759439 0.249667052 0.239868427 0.186228344
## [816] 0.349047337 0.154985496 0.336519388 0.275646624 0.156932934
## [821] 0.460680853 0.214162042 0.489593596 0.153481109 0.421199021
## [826] 0.251443119 0.212674829 0.446543954 0.305162831 0.113356084
## [831] 0.247728952 0.276523057 0.311459021 0.315822901 0.418493729
## [836] 0.245509860 0.280530176 0.255736785 0.283143611 0.388672158
## [841] 0.332873253 0.333516078 0.321069717 0.407379696 0.214481430
## [846] 0.306105834 0.266653438 0.355029913 0.306577464 0.406099214
## [851] 0.339740392 0.429973324 0.329561015 0.360245461 0.158107403
## [856] 0.340006144 0.477567032 0.287192977 0.341122277 0.241345137
## [861] 0.302799032 0.388882831 0.390973086 0.286398322 0.118554876
## [866] 0.441798609 0.166373798 0.480805729 0.325817976 0.161636200
## [871] 0.296988589 0.263789344 0.499208655 0.166684598 0.279686651
## [876] 0.514063740 0.273381451 0.328818353 0.250966970 0.511111646
## [881] 0.385366578 0.402194261 0.242956049 0.247356310 0.279660442
## [886] 0.265530777 0.248638545 0.179862247 0.175537609 0.294560696
## [891] 0.223823148 0.268236908 0.267491644 0.323596137 0.422881689
## [896] 0.406017539 0.343438289 0.246171985 0.207847731 0.258340392
## [901] 0.097566477 0.280844654 0.336349968 0.416344447 0.367247672
## [906] 0.130818111 0.302734251 0.339216221 0.044812842 0.214946104
## [911] 0.376476903 0.287665101 0.394846855 0.455058335 0.289176351
## [916] 0.135707286 0.192037101 0.533690228 0.198640539 0.393546021
## [921] 0.285208264 0.247623892 0.364387527 0.553981485 0.232394362
## [926] 0.291070225 0.464044895 0.332977699 0.277188258 0.262265652
## [931] 0.158601756 0.261526949 0.271292349 0.284864476 0.358869723
## [936] 0.325897139 0.414483638 0.327050821 0.232603701 0.315733285
## [941] 0.221910920 0.273957887 0.239429676 0.316403214 0.309880789
## [946] 0.388573231 0.344127429 0.404822853 0.266407203 0.319840749
## [951] 0.086964886 0.342335630 0.381431127 0.341954435 0.291516536
## [956] 0.230305903 0.246643974 0.446020569 0.446133921 0.361227475
## [961] 0.249183758 0.451269541 0.260010688 0.241189410 0.412330899
## [966] 0.288291772 0.290773489 0.378477882 0.177457192 0.236871546
## [971] 0.329085890 0.523503885 0.215155030 0.381584463 0.271442332
## [976] 0.340619369 0.395797405 0.227235510 0.137459051 0.366477565
## [981] 0.361499189 0.117656835 0.400840082 0.328301650 0.272497364
## [986] 0.169789690 0.396401272 0.260710975 0.340611624 0.403418049
## [991] 0.186061967 0.254762387 0.340783951 0.233991774 0.314620882
## [996] 0.086634587 0.118069324 0.370651342 0.339438785 0.340991446hist(sampling.dist$slope)
table(sampling.dist$sig)##
## 0 1
## 133 867mean(sampling.dist$sig)## [1] 0.867 #This represents power for the model
Social Deprivation as Moderator